2025-03-14T04:12:09.0348815Z Current runner version: '2.322.0'
2025-03-14T04:12:09.0354617Z Runner name: 'i-00f46405241e0bbc9'
2025-03-14T04:12:09.0355390Z Runner group name: 'Default'
2025-03-14T04:12:09.0356138Z Machine name: 'ip-10-0-63-215'
2025-03-14T04:12:09.0359363Z ##[group]GITHUB_TOKEN Permissions
2025-03-14T04:12:09.0362180Z Actions: read
2025-03-14T04:12:09.0362697Z Attestations: read
2025-03-14T04:12:09.0363175Z Checks: read
2025-03-14T04:12:09.0363639Z Contents: read
2025-03-14T04:12:09.0364058Z Deployments: read
2025-03-14T04:12:09.0364632Z Discussions: read
2025-03-14T04:12:09.0365114Z Issues: read
2025-03-14T04:12:09.0365555Z Metadata: read
2025-03-14T04:12:09.0366005Z Packages: read
2025-03-14T04:12:09.0366494Z Pages: read
2025-03-14T04:12:09.0366961Z PullRequests: read
2025-03-14T04:12:09.0367455Z RepositoryProjects: read
2025-03-14T04:12:09.0367981Z SecurityEvents: read
2025-03-14T04:12:09.0368475Z Statuses: read
2025-03-14T04:12:09.0368967Z ##[endgroup]
2025-03-14T04:12:09.0372400Z Secret source: Actions
2025-03-14T04:12:09.0373326Z Prepare workflow directory
2025-03-14T04:12:09.3714809Z Prepare all required actions
2025-03-14T04:12:09.3754265Z Getting action download info
2025-03-14T04:12:09.5634637Z Download action repository 'pytorch/test-infra@main' (SHA:de00dac6adc071cb2f9861380a0ed3947b93e5cc)
2025-03-14T04:12:10.7404170Z Download action repository 'pytorch/pytorch@main' (SHA:e5679009988279a3059f398ed2077a0477099ba6)
2025-03-14T04:12:25.2981359Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0)
2025-03-14T04:12:25.5064660Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a)
2025-03-14T04:12:25.7563759Z Getting action download info
2025-03-14T04:12:25.8560940Z Download action repository 'actions/checkout@v4' (SHA:11bd71901bbe5b1630ceea73d27597364c9af683)
2025-03-14T04:12:26.0975898Z Getting action download info
2025-03-14T04:12:26.1935782Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e)
2025-03-14T04:12:26.3502145Z Getting action download info
2025-03-14T04:12:26.4571840Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482)
2025-03-14T04:12:26.6032223Z Getting action download info
2025-03-14T04:12:26.7236690Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/heads/main (aed0b7a742a2d7b7901790622829cbd2135049a4)
2025-03-14T04:12:26.7238388Z ##[group] Inputs
2025-03-14T04:12:26.7238653Z   build-environment: linux-jammy-py3.9-gcc11-build
2025-03-14T04:12:26.7240325Z   test-matrix: {"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]}
2025-03-14T04:12:26.7242181Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:26.7242700Z   sync-tag: 
2025-03-14T04:12:26.7243306Z   timeout-minutes: 240
2025-03-14T04:12:26.7243497Z   use-gha: 
2025-03-14T04:12:26.7243674Z   dashboard-tag: 
2025-03-14T04:12:26.7243863Z   s3-bucket: gha-artifacts
2025-03-14T04:12:26.7244071Z   aws-role-to-assume: 
2025-03-14T04:12:26.7244702Z   disable-monitor: false
2025-03-14T04:12:26.7244990Z ##[endgroup]
2025-03-14T04:12:26.7245352Z Complete job name: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:12:26.7633426Z A job started hook has been configured by the self-hosted runner administrator
2025-03-14T04:12:26.7704011Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh'
2025-03-14T04:12:26.7710529Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:26.7710952Z ##[endgroup]
2025-03-14T04:12:28.0269432Z Runner Type: linux.8xlarge.amx
2025-03-14T04:12:28.0269908Z Instance Type: m7i-flex.8xlarge
2025-03-14T04:12:28.0270137Z AMI Name: unknown
2025-03-14T04:12:28.0299134Z AMI ID: ami-08b5b3a93ed654d19
2025-03-14T04:12:32.2396188Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main
2025-03-14T04:12:32.2396519Z with:
2025-03-14T04:12:32.2397192Z   github-secret: ***
2025-03-14T04:12:32.2397650Z   instructions: All testing is done inside the container, to start an interactive session run:
  docker exec -it $(docker container ps --format '{{.ID}}') bash

2025-03-14T04:12:32.2398131Z   activate-with-label: false
2025-03-14T04:12:32.2398332Z   label: with-ssh
2025-03-14T04:12:32.2398514Z   remove-existing-keys: true
2025-03-14T04:12:32.2398713Z   fail-silently: true
2025-03-14T04:12:32.2398889Z env:
2025-03-14T04:12:32.2399054Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:32.2399244Z ##[endgroup]
2025-03-14T04:12:32.3405734Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info.
2025-03-14T04:12:32.3406374Z Not on pull request and ciflow reference could not be extracted, skipping adding ssh keys
2025-03-14T04:12:32.3541806Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main
2025-03-14T04:12:32.3542131Z with:
2025-03-14T04:12:32.3542318Z   no-sudo: true
2025-03-14T04:12:32.3542510Z   submodules: recursive
2025-03-14T04:12:32.3542721Z   fetch-depth: 0
2025-03-14T04:12:32.3542896Z env:
2025-03-14T04:12:32.3543077Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:32.3543298Z ##[endgroup]
2025-03-14T04:12:32.3615176Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:12:32.3615789Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:12:32.3623595Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:32.3623876Z env:
2025-03-14T04:12:32.3624087Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:32.3624325Z ##[endgroup]
2025-03-14T04:12:32.3702137Z ##[group]Run # Use all available CPUs for fetching
2025-03-14T04:12:32.3702470Z # Use all available CPUs for fetching
2025-03-14T04:12:32.3702710Z cd "${GITHUB_WORKSPACE}"
2025-03-14T04:12:32.3702951Z git config --global fetch.parallel 0
2025-03-14T04:12:32.3703216Z git config --global submodule.fetchJobs 0
2025-03-14T04:12:32.3703468Z 
2025-03-14T04:12:32.3703717Z # Clean workspace. The default checkout action should also do this, but
2025-03-14T04:12:32.3704028Z # do it here as well just in case
2025-03-14T04:12:32.3704252Z if [[ -d .git ]]; then
2025-03-14T04:12:32.3704459Z   if [ -z "${NO_SUDO}" ]; then
2025-03-14T04:12:32.3704680Z     sudo git clean -ffdx
2025-03-14T04:12:32.3704882Z   else
2025-03-14T04:12:32.3705061Z     git clean -ffdx
2025-03-14T04:12:32.3705254Z   fi
2025-03-14T04:12:32.3705419Z fi
2025-03-14T04:12:32.3709426Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:32.3709680Z env:
2025-03-14T04:12:32.3709855Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:32.3710049Z   NO_SUDO: true
2025-03-14T04:12:32.3710218Z ##[endgroup]
2025-03-14T04:12:32.8516174Z Removing .additional_ci_files/
2025-03-14T04:12:32.8516576Z Removing benchmarks/dynamo/__pycache__/
2025-03-14T04:12:32.8516834Z Removing build/
2025-03-14T04:12:32.8517450Z Removing dist/
2025-03-14T04:12:32.8517822Z Removing logs-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38752604198.zip
2025-03-14T04:12:32.8518344Z Removing test-jsons-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38752604198.zip
2025-03-14T04:12:32.8518875Z Removing test-reports-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38752604198.zip
2025-03-14T04:12:32.8519269Z Removing test/test-reports/
2025-03-14T04:12:32.8519494Z Removing tools/__pycache__/
2025-03-14T04:12:32.8519713Z Removing tools/stats/__pycache__/
2025-03-14T04:12:32.8519984Z Removing tools/stats/upload_utilization_stats/__pycache__/
2025-03-14T04:12:32.8520239Z Removing torchbench/
2025-03-14T04:12:32.8520416Z Removing usage_log.txt
2025-03-14T04:12:32.8588333Z ##[group]Run actions/checkout@v4
2025-03-14T04:12:32.8588561Z with:
2025-03-14T04:12:32.8588767Z   ref: aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:12:32.8589011Z   fetch-depth: 0
2025-03-14T04:12:32.8589221Z   submodules: recursive
2025-03-14T04:12:32.8589408Z   show-progress: false
2025-03-14T04:12:32.8589598Z   repository: pytorch/pytorch
2025-03-14T04:12:32.8589857Z   token: ***
2025-03-14T04:12:32.8590021Z   ssh-strict: true
2025-03-14T04:12:32.8590190Z   ssh-user: git
2025-03-14T04:12:32.8590364Z   persist-credentials: true
2025-03-14T04:12:32.8590556Z   clean: true
2025-03-14T04:12:32.8590731Z   sparse-checkout-cone-mode: true
2025-03-14T04:12:32.8590936Z   fetch-tags: false
2025-03-14T04:12:32.8591106Z   lfs: false
2025-03-14T04:12:32.8591267Z   set-safe-directory: true
2025-03-14T04:12:32.8591451Z env:
2025-03-14T04:12:32.8591825Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:32.8592017Z ##[endgroup]
2025-03-14T04:12:32.9530023Z Syncing repository: pytorch/pytorch
2025-03-14T04:12:32.9531070Z ##[group]Getting Git version info
2025-03-14T04:12:32.9531554Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch'
2025-03-14T04:12:32.9532053Z [command]/usr/bin/git version
2025-03-14T04:12:32.9532666Z git version 2.47.1
2025-03-14T04:12:32.9551779Z ##[endgroup]
2025-03-14T04:12:32.9559918Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/bc4b74ab-d54f-44c6-9e7a-22a34b493e82/.gitconfig'
2025-03-14T04:12:32.9584293Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/bc4b74ab-d54f-44c6-9e7a-22a34b493e82' before making global git config changes
2025-03-14T04:12:32.9588142Z Adding repository directory to the temporary git global config as a safe directory
2025-03-14T04:12:32.9588915Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-14T04:12:32.9638406Z [command]/usr/bin/git config --local --get remote.origin.url
2025-03-14T04:12:32.9650557Z https://github.com/pytorch/pytorch
2025-03-14T04:12:32.9670875Z ##[group]Removing previously created refs, to avoid conflicts
2025-03-14T04:12:32.9676558Z [command]/usr/bin/git rev-parse --symbolic-full-name --verify --quiet HEAD
2025-03-14T04:12:32.9693347Z HEAD
2025-03-14T04:12:32.9724768Z ##[endgroup]
2025-03-14T04:12:32.9725011Z [command]/usr/bin/git submodule status
2025-03-14T04:12:33.0043345Z  7e1e1fe3858c63c251c637ae41a20de425dde96f android/libs/fbjni (v0.1.0-12-g7e1e1fe)
2025-03-14T04:12:33.0114174Z  4dfe081cf6bcd15db339cf2680b9281b8451eeb3 third_party/FP16 (4dfe081)
2025-03-14T04:12:33.0196219Z  b408327ac2a15ec3e43352421954f5b1967701d1 third_party/FXdiv (b408327)
2025-03-14T04:12:33.0275979Z  c07e3a0400713d546e0dea2d5466dd22ea389c73 third_party/NNPACK (c07e3a0)
2025-03-14T04:12:33.0294443Z  e170594ac7cf1dac584da473d4ca9301087090c1 third_party/NVTX (v3.1.0)
2025-03-14T04:12:33.0357807Z  a6bfc237255a6bac1513f7c1ebde6d8aed6b5191 third_party/VulkanMemoryAllocator (v2.1.0-705-ga6bfc23)
2025-03-14T04:12:33.0799891Z  51a0103656eff6fc9bfd39a4597923c4b542c883 third_party/XNNPACK (remotes/origin/ds/ndk-1243-g51a010365)
2025-03-14T04:12:33.0822730Z  0d98dba29d66e93259db7daa53a9327df767a415 third_party/benchmark (v1.6.1)
2025-03-14T04:12:33.0846283Z  8086bbe3a78d931eb96fe12fdc014082e18d18d3 third_party/composable_kernel (mock-tag-test-6-g8086bbe3a)
2025-03-14T04:12:33.0962705Z  3b6597bba913d51161383657829b7e644e59c006 third_party/cpp-httplib (v0.15.3-20-g3b6597b)
2025-03-14T04:12:33.1059936Z  1e83a2fdd3102f65c6f1fb602c1b320486218a99 third_party/cpuinfo (1e83a2f)
2025-03-14T04:12:33.1089797Z  91b7532f3386768bba4f444ee7672b497f34da8a third_party/cudnn_frontend (v0.5-44-g91b7532)
2025-03-14T04:12:33.1173042Z  afa1772203677c5118fcd82537a9c8fefbcc7008 third_party/cutlass (v3.8.0)
2025-03-14T04:12:33.1751127Z  3147391d946bb4b6c68edd901f2add6ac1f31f8c third_party/eigen (3.4.0)
2025-03-14T04:12:33.2062006Z  dbc3157bf256f1339b3fa1fef2be89ac4078be0e third_party/fbgemm (v0.4.1-446-gdbc3157b)
2025-03-14T04:12:33.2170388Z  979702c87a8713a8e0a5e9fee122b90d2ef13be5 third_party/flash-attention (v2.7.4)
2025-03-14T04:12:33.2190155Z  01834de25e4bf3975a9a00e816292b1ad0fe184b third_party/flatbuffers (v23.3.3)
2025-03-14T04:12:33.2566733Z  123913715afeb8a437e6388b4473fcc4753e1c9a third_party/fmt (11.1.4)
2025-03-14T04:12:33.2659837Z  3fb5c176c17c765a3492cd2f0321b0dab712f350 third_party/gemmlowp/gemmlowp (remotes/origin/revert-87-master-135-g3fb5c17)
2025-03-14T04:12:33.2753713Z  5354032ea08eadd7fc4456477f7f7c6308818509 third_party/gloo (5354032)
2025-03-14T04:12:33.2965304Z  b514bdc898e2951020cbdca1304b75f5950d1f59 third_party/googletest (release-1.8.0-3484-gb514bdc8)
2025-03-14T04:12:33.3035737Z  719d8e6cd7f7a0e01b155657526d693acf97c2b3 third_party/ideep (pytorch-rls-v3.7.1)
2025-03-14T04:12:33.3086145Z  5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42 third_party/ittapi (v3.23.0-14-g5b8a7d7)
2025-03-14T04:12:33.3316874Z  2859721fd9e73d3ca1c56f827dbc64e6d68f78a2 third_party/kineto (remotes/origin/sraikund/test-53-g2859721)
2025-03-14T04:12:33.3335097Z  ef685a13cfbe8d418aa2ed34350e21e4938358b6 third_party/kleidiai (v1.3.0)
2025-03-14T04:12:33.3354138Z  b66e3214d8a104669c2ec05ae91ebc26a8f5ab78 third_party/mimalloc (v1.8.2)
2025-03-14T04:12:33.3783048Z  87cda1d6646592ac5866dc703c8e1839046a6806 third_party/nlohmann (v3.10.1-113-g87cda1d6)
2025-03-14T04:12:33.4052776Z  b8baa8446686496da4cc8fda09f2b6fe65c2a02c third_party/onnx (v1.17.0)
2025-03-14T04:12:33.4071859Z  a799f4aed9c94b765dcdaabaeab7d5e7e2310878 third_party/opentelemetry-cpp (v1.14.2)
2025-03-14T04:12:33.4092820Z  9d3ab05a7fffbc71a492bc6a17be034e83e8f0fe third_party/pocketfft (release_for_eigen-11-g9d3ab05)
2025-03-14T04:12:33.4518528Z  d1eca4e4b421cd2997495c4b4e65cea6be4e9b8a third_party/protobuf (v3.7.0-rc.2-1279-gd1eca4e4b)
2025-03-14T04:12:33.4544868Z  072586a71b55b7f8c584153d223e95687148a900 third_party/psimd (heads/master)
2025-03-14T04:12:33.4583856Z  4fe0e1e183925bf8cfa6aae24237e724a96479b8 third_party/pthreadpool (0.1-144-g4fe0e1e)
2025-03-14T04:12:33.4632665Z  a2e59f0e7065404b44dfe92a28aca47ba1378dc4 third_party/pybind11 (v2.11.0-182-ga2e59f0e)
2025-03-14T04:12:33.4700767Z  f45429b087dd7d5bc78bb40dc7cf06425c252d67 third_party/python-peachpy (remotes/origin/pre-generated)
2025-03-14T04:12:33.4805214Z  56e1f79cb140fb9326d612d0be06b5250565cade third_party/sleef (3.7-33-g56e1f79)
2025-03-14T04:12:33.4870105Z  52791a2fd214b2a9dc5759d36725909c1daa7f2e third_party/tensorpipe (remotes/origin/master)
2025-03-14T04:12:33.4884733Z ##[group]Cleaning the repository
2025-03-14T04:12:33.4888090Z [command]/usr/bin/git clean -ffdx
2025-03-14T04:12:33.5043502Z [command]/usr/bin/git reset --hard HEAD
2025-03-14T04:12:34.4882864Z HEAD is now at 2cddff7538a avoid require_contiguous on mkldnn tensor
2025-03-14T04:12:34.4929522Z ##[endgroup]
2025-03-14T04:12:34.4929907Z ##[group]Disabling automatic garbage collection
2025-03-14T04:12:34.4935038Z [command]/usr/bin/git config --local gc.auto 0
2025-03-14T04:12:34.4971621Z ##[endgroup]
2025-03-14T04:12:34.4971995Z ##[group]Setting up auth
2025-03-14T04:12:34.4977890Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2025-03-14T04:12:34.5006746Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :"
2025-03-14T04:12:34.5292621Z Entering 'android/libs/fbjni'
2025-03-14T04:12:34.5340279Z Entering 'third_party/FP16'
2025-03-14T04:12:34.5390358Z Entering 'third_party/FXdiv'
2025-03-14T04:12:34.5437656Z Entering 'third_party/NNPACK'
2025-03-14T04:12:34.5488407Z Entering 'third_party/NVTX'
2025-03-14T04:12:34.5533776Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:12:34.5594561Z Entering 'third_party/XNNPACK'
2025-03-14T04:12:34.5648783Z Entering 'third_party/benchmark'
2025-03-14T04:12:34.5698389Z Entering 'third_party/composable_kernel'
2025-03-14T04:12:34.5755419Z Entering 'third_party/cpp-httplib'
2025-03-14T04:12:34.5804345Z Entering 'third_party/cpuinfo'
2025-03-14T04:12:34.5853053Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:12:34.5900930Z Entering 'third_party/cutlass'
2025-03-14T04:12:34.5952720Z Entering 'third_party/eigen'
2025-03-14T04:12:34.6003100Z Entering 'third_party/fbgemm'
2025-03-14T04:12:34.6053085Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:12:34.6106363Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:12:34.6150094Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:12:34.6210597Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:12:34.6254393Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:12:34.6308252Z Entering 'third_party/flash-attention'
2025-03-14T04:12:34.6357965Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:12:34.6409227Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:12:34.6468720Z Entering 'third_party/flatbuffers'
2025-03-14T04:12:34.6515765Z Entering 'third_party/fmt'
2025-03-14T04:12:34.6560483Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:12:34.6609069Z Entering 'third_party/gloo'
2025-03-14T04:12:34.6653756Z Entering 'third_party/googletest'
2025-03-14T04:12:34.6708838Z Entering 'third_party/ideep'
2025-03-14T04:12:34.6756164Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:12:34.6812819Z Entering 'third_party/ittapi'
2025-03-14T04:12:34.6862652Z Entering 'third_party/kineto'
2025-03-14T04:12:34.6911644Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:12:34.6960859Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:12:34.7013781Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:12:34.7062650Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:12:34.7109806Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:12:34.7155352Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:12:34.7208591Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:12:34.7255286Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:12:34.7306675Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:12:34.7354613Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:12:34.7402950Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:12:34.7449524Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:12:34.7497867Z Entering 'third_party/kleidiai'
2025-03-14T04:12:34.7548703Z Entering 'third_party/mimalloc'
2025-03-14T04:12:34.7605512Z Entering 'third_party/nlohmann'
2025-03-14T04:12:34.7654539Z Entering 'third_party/onnx'
2025-03-14T04:12:34.7715926Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:12:34.7769153Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:12:34.7818747Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:12:34.7869964Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:12:34.7912349Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:12:34.7958598Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:12:34.8006903Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:12:34.8051745Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:12:34.8098389Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:12:34.8143625Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:12:34.8193405Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:12:34.8238484Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:12:34.8302772Z Entering 'third_party/pocketfft'
2025-03-14T04:12:34.8349966Z Entering 'third_party/protobuf'
2025-03-14T04:12:34.8404174Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:12:34.8449425Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:12:34.8502325Z Entering 'third_party/psimd'
2025-03-14T04:12:34.8546955Z Entering 'third_party/pthreadpool'
2025-03-14T04:12:34.8594749Z Entering 'third_party/pybind11'
2025-03-14T04:12:34.8644264Z Entering 'third_party/python-peachpy'
2025-03-14T04:12:34.8693866Z Entering 'third_party/sleef'
2025-03-14T04:12:34.8741141Z Entering 'third_party/tensorpipe'
2025-03-14T04:12:34.8788496Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:12:34.8835586Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:12:34.8886272Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:12:34.8931818Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:12:34.8974062Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:12:34.9044046Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2025-03-14T04:12:34.9075203Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :"
2025-03-14T04:12:34.9352763Z Entering 'android/libs/fbjni'
2025-03-14T04:12:34.9398303Z Entering 'third_party/FP16'
2025-03-14T04:12:34.9444459Z Entering 'third_party/FXdiv'
2025-03-14T04:12:34.9491779Z Entering 'third_party/NNPACK'
2025-03-14T04:12:34.9538616Z Entering 'third_party/NVTX'
2025-03-14T04:12:34.9589781Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:12:34.9634545Z Entering 'third_party/XNNPACK'
2025-03-14T04:12:34.9702759Z Entering 'third_party/benchmark'
2025-03-14T04:12:34.9752845Z Entering 'third_party/composable_kernel'
2025-03-14T04:12:34.9805989Z Entering 'third_party/cpp-httplib'
2025-03-14T04:12:34.9869747Z Entering 'third_party/cpuinfo'
2025-03-14T04:12:34.9920873Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:12:34.9971901Z Entering 'third_party/cutlass'
2025-03-14T04:12:35.0023471Z Entering 'third_party/eigen'
2025-03-14T04:12:35.0073804Z Entering 'third_party/fbgemm'
2025-03-14T04:12:35.0121200Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:12:35.0168411Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:12:35.0220528Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:12:35.0277273Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:12:35.0320982Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:12:35.0371852Z Entering 'third_party/flash-attention'
2025-03-14T04:12:35.0423892Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:12:35.0479123Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:12:35.0537852Z Entering 'third_party/flatbuffers'
2025-03-14T04:12:35.0593409Z Entering 'third_party/fmt'
2025-03-14T04:12:35.0641358Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:12:35.0689887Z Entering 'third_party/gloo'
2025-03-14T04:12:35.0736382Z Entering 'third_party/googletest'
2025-03-14T04:12:35.0787004Z Entering 'third_party/ideep'
2025-03-14T04:12:35.0834261Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:12:35.0895078Z Entering 'third_party/ittapi'
2025-03-14T04:12:35.0947583Z Entering 'third_party/kineto'
2025-03-14T04:12:35.0994393Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:12:35.1049673Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:12:35.1099663Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:12:35.1146385Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:12:35.1192663Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:12:35.1250663Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:12:35.1303329Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:12:35.1351008Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:12:35.1398569Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:12:35.1510971Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:12:35.1560125Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:12:35.1605826Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:12:35.1654340Z Entering 'third_party/kleidiai'
2025-03-14T04:12:35.1706801Z Entering 'third_party/mimalloc'
2025-03-14T04:12:35.1758049Z Entering 'third_party/nlohmann'
2025-03-14T04:12:35.1806685Z Entering 'third_party/onnx'
2025-03-14T04:12:35.1877141Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:12:35.1935764Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:12:35.1990248Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:12:35.2037623Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:12:35.2094461Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:12:35.2153735Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:12:35.2203241Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:12:35.2251621Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:12:35.2298560Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:12:35.2346929Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:12:35.2393226Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:12:35.2445324Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:12:35.2511044Z Entering 'third_party/pocketfft'
2025-03-14T04:12:35.2561296Z Entering 'third_party/protobuf'
2025-03-14T04:12:35.2614993Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:12:35.2691955Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:12:35.2741856Z Entering 'third_party/psimd'
2025-03-14T04:12:35.2789057Z Entering 'third_party/pthreadpool'
2025-03-14T04:12:35.2837165Z Entering 'third_party/pybind11'
2025-03-14T04:12:35.2887167Z Entering 'third_party/python-peachpy'
2025-03-14T04:12:35.2934846Z Entering 'third_party/sleef'
2025-03-14T04:12:35.2990791Z Entering 'third_party/tensorpipe'
2025-03-14T04:12:35.3034396Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:12:35.3080753Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:12:35.3126806Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:12:35.3174714Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:12:35.3221553Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:12:35.3292427Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic ***
2025-03-14T04:12:35.3346567Z ##[endgroup]
2025-03-14T04:12:35.3349001Z ##[group]Fetching the repository
2025-03-14T04:12:35.3352751Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/*
2025-03-14T04:12:35.5580551Z From https://github.com/pytorch/pytorch
2025-03-14T04:12:35.5582045Z  - [deleted]                 (none)                  -> ciflow/inductor/149162
2025-03-14T04:12:35.5840634Z  - [deleted]                 (none)                  -> ciflow/trunk/149166
2025-03-14T04:12:36.7617250Z    b4745db9048..e5679009988  main                    -> origin/main
2025-03-14T04:12:36.7626479Z  + e2f3d057bea...c4b8be3d09f update_submodule_FBGEMM -> origin/update_submodule_FBGEMM  (forced update)
2025-03-14T04:12:36.7627180Z    4098a229a04..49570cb4024  viable/strict           -> origin/viable/strict
2025-03-14T04:12:36.7628237Z    1579a02c823..3ffd7552df1  wdvr/iss145259_alt      -> origin/wdvr/iss145259_alt
2025-03-14T04:12:36.7635905Z  * [new tag]                 ciflow/trunk/146289     -> ciflow/trunk/146289
2025-03-14T04:12:36.7636434Z  * [new tag]                 ciflow/trunk/149064     -> ciflow/trunk/149064
2025-03-14T04:12:36.8008381Z [command]/usr/bin/git rev-parse --verify --quiet aed0b7a742a2d7b7901790622829cbd2135049a4^{object}
2025-03-14T04:12:36.8032645Z aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:12:36.8035133Z ##[endgroup]
2025-03-14T04:12:36.8035453Z ##[group]Determining the checkout info
2025-03-14T04:12:36.8035799Z ##[endgroup]
2025-03-14T04:12:36.8039855Z [command]/usr/bin/git sparse-checkout disable
2025-03-14T04:12:37.0242608Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig
2025-03-14T04:12:37.0271962Z ##[group]Checking out the ref
2025-03-14T04:12:37.0279096Z [command]/usr/bin/git checkout --progress --force aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:12:37.1234603Z Previous HEAD position was 2cddff7538a avoid require_contiguous on mkldnn tensor
2025-03-14T04:12:37.1239900Z HEAD is now at aed0b7a742a [c10d] Add param recording for uniqueID broadcasting and allgather (#149166)
2025-03-14T04:12:37.1261781Z ##[endgroup]
2025-03-14T04:12:37.1262149Z ##[group]Setting up auth for fetching submodules
2025-03-14T04:12:37.1268151Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic ***
2025-03-14T04:12:37.1324698Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf
2025-03-14T04:12:37.1347032Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com:
2025-03-14T04:12:37.1370124Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com:
2025-03-14T04:12:37.1392102Z ##[endgroup]
2025-03-14T04:12:37.1392526Z ##[group]Fetching submodules
2025-03-14T04:12:37.1393485Z [command]/usr/bin/git submodule sync --recursive
2025-03-14T04:12:37.1667235Z Synchronizing submodule url for 'android/libs/fbjni'
2025-03-14T04:12:37.1688157Z Synchronizing submodule url for 'third_party/FP16'
2025-03-14T04:12:37.1704638Z Synchronizing submodule url for 'third_party/FXdiv'
2025-03-14T04:12:37.1723364Z Synchronizing submodule url for 'third_party/NNPACK'
2025-03-14T04:12:37.1738551Z Synchronizing submodule url for 'third_party/NVTX'
2025-03-14T04:12:37.1758911Z Synchronizing submodule url for 'third_party/VulkanMemoryAllocator'
2025-03-14T04:12:37.1783157Z Synchronizing submodule url for 'third_party/XNNPACK'
2025-03-14T04:12:37.1808562Z Synchronizing submodule url for 'third_party/benchmark'
2025-03-14T04:12:37.1843432Z Synchronizing submodule url for 'third_party/composable_kernel'
2025-03-14T04:12:37.1845591Z Synchronizing submodule url for 'third_party/cpp-httplib'
2025-03-14T04:12:37.1865376Z Synchronizing submodule url for 'third_party/cpuinfo'
2025-03-14T04:12:37.1885321Z Synchronizing submodule url for 'third_party/cudnn_frontend'
2025-03-14T04:12:37.1901635Z Synchronizing submodule url for 'third_party/cutlass'
2025-03-14T04:12:37.1929966Z Synchronizing submodule url for 'third_party/eigen'
2025-03-14T04:12:37.1947239Z Synchronizing submodule url for 'third_party/fbgemm'
2025-03-14T04:12:37.1965564Z Synchronizing submodule url for 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:12:37.1989267Z Synchronizing submodule url for 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:12:37.2006338Z Synchronizing submodule url for 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:12:37.2030655Z Synchronizing submodule url for 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:12:37.2043560Z Synchronizing submodule url for 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:12:37.2059894Z Synchronizing submodule url for 'third_party/flash-attention'
2025-03-14T04:12:37.2080630Z Synchronizing submodule url for 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:12:37.2100030Z Synchronizing submodule url for 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:12:37.2130145Z Synchronizing submodule url for 'third_party/flatbuffers'
2025-03-14T04:12:37.2169319Z Synchronizing submodule url for 'third_party/fmt'
2025-03-14T04:12:37.2169871Z Synchronizing submodule url for 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:12:37.2189472Z Synchronizing submodule url for 'third_party/gloo'
2025-03-14T04:12:37.2208456Z Synchronizing submodule url for 'third_party/googletest'
2025-03-14T04:12:37.2227209Z Synchronizing submodule url for 'third_party/ideep'
2025-03-14T04:12:37.2242120Z Synchronizing submodule url for 'third_party/ideep/mkl-dnn'
2025-03-14T04:12:37.2269220Z Synchronizing submodule url for 'third_party/ittapi'
2025-03-14T04:12:37.2287587Z Synchronizing submodule url for 'third_party/kineto'
2025-03-14T04:12:37.2307079Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:12:37.2319169Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:12:37.2334549Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:12:37.2353935Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:12:37.2377714Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:12:37.2393338Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:12:37.2411055Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:12:37.2441870Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:12:37.2447345Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:12:37.2466100Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:12:37.2485812Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:12:37.2506338Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:12:37.2537785Z Synchronizing submodule url for 'third_party/kleidiai'
2025-03-14T04:12:37.2543733Z Synchronizing submodule url for 'third_party/mimalloc'
2025-03-14T04:12:37.2558203Z Synchronizing submodule url for 'third_party/nlohmann'
2025-03-14T04:12:37.2580035Z Synchronizing submodule url for 'third_party/onnx'
2025-03-14T04:12:37.2611142Z Synchronizing submodule url for 'third_party/onnx/third_party/pybind11'
2025-03-14T04:12:37.2630069Z Synchronizing submodule url for 'third_party/opentelemetry-cpp'
2025-03-14T04:12:37.2644785Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:12:37.2671098Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:12:37.2688433Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:12:37.2715954Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:12:37.2726307Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:12:37.2740510Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:12:37.2760716Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:12:37.2778296Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:12:37.2798101Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:12:37.2820443Z Synchronizing submodule url for 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:12:37.2851487Z Synchronizing submodule url for 'third_party/pocketfft'
2025-03-14T04:12:37.2868713Z Synchronizing submodule url for 'third_party/protobuf'
2025-03-14T04:12:37.2888068Z Synchronizing submodule url for 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:12:37.2905464Z Synchronizing submodule url for 'third_party/protobuf/third_party/googletest'
2025-03-14T04:12:37.2924999Z Synchronizing submodule url for 'third_party/psimd'
2025-03-14T04:12:37.2941416Z Synchronizing submodule url for 'third_party/pthreadpool'
2025-03-14T04:12:37.2961341Z Synchronizing submodule url for 'third_party/pybind11'
2025-03-14T04:12:37.2979106Z Synchronizing submodule url for 'third_party/python-peachpy'
2025-03-14T04:12:37.2999670Z Synchronizing submodule url for 'third_party/sleef'
2025-03-14T04:12:37.3022920Z Synchronizing submodule url for 'third_party/tensorpipe'
2025-03-14T04:12:37.3035575Z Synchronizing submodule url for 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:12:37.3046959Z Synchronizing submodule url for 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:12:37.3067115Z Synchronizing submodule url for 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:12:37.3084890Z Synchronizing submodule url for 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:12:37.3099470Z Synchronizing submodule url for 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:12:37.3131541Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive
2025-03-14T04:12:37.3556267Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f'
2025-03-14T04:12:37.3653657Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3'
2025-03-14T04:12:37.3726264Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1'
2025-03-14T04:12:37.3927908Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73'
2025-03-14T04:12:37.4220856Z Submodule path 'third_party/NVTX': checked out 'e170594ac7cf1dac584da473d4ca9301087090c1'
2025-03-14T04:12:37.4532842Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191'
2025-03-14T04:12:38.0608223Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883'
2025-03-14T04:12:38.0813333Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415'
2025-03-14T04:12:38.2963832Z Submodule path 'third_party/composable_kernel': checked out '8086bbe3a78d931eb96fe12fdc014082e18d18d3'
2025-03-14T04:12:38.3324980Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006'
2025-03-14T04:12:38.4181882Z Submodule path 'third_party/cpuinfo': checked out '1e83a2fdd3102f65c6f1fb602c1b320486218a99'
2025-03-14T04:12:38.4488497Z Submodule path 'third_party/cudnn_frontend': checked out '91b7532f3386768bba4f444ee7672b497f34da8a'
2025-03-14T04:12:38.9805059Z Submodule path 'third_party/cutlass': checked out 'afa1772203677c5118fcd82537a9c8fefbcc7008'
2025-03-14T04:12:39.2158428Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c'
2025-03-14T04:12:39.2730797Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e'
2025-03-14T04:12:39.3122674Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81'
2025-03-14T04:12:39.3968477Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3'
2025-03-14T04:12:39.7623525Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6'
2025-03-14T04:12:39.8021396Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796'
2025-03-14T04:12:39.8128252Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1'
2025-03-14T04:12:39.8812914Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5'
2025-03-14T04:12:40.0931908Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33'
2025-03-14T04:12:40.6103293Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420'
2025-03-14T04:12:40.7320241Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b'
2025-03-14T04:12:40.7608647Z Submodule path 'third_party/fmt': checked out '123913715afeb8a437e6388b4473fcc4753e1c9a'
2025-03-14T04:12:40.7942682Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350'
2025-03-14T04:12:40.8165360Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509'
2025-03-14T04:12:40.8572539Z Submodule path 'third_party/googletest': checked out 'b514bdc898e2951020cbdca1304b75f5950d1f59'
2025-03-14T04:12:40.8685593Z Submodule path 'third_party/ideep': checked out '719d8e6cd7f7a0e01b155657526d693acf97c2b3'
2025-03-14T04:12:41.3597858Z Submodule path 'third_party/ideep/mkl-dnn': checked out '8d263e693366ef8db40acc569cc7d8edf644556d'
2025-03-14T04:12:41.3740857Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42'
2025-03-14T04:12:41.4576944Z Submodule path 'third_party/kineto': checked out '2859721fd9e73d3ca1c56f827dbc64e6d68f78a2'
2025-03-14T04:12:41.5311582Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e'
2025-03-14T04:12:41.6959456Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9'
2025-03-14T04:12:41.7120558Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400'
2025-03-14T04:12:41.7439655Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05'
2025-03-14T04:12:41.7559360Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067'
2025-03-14T04:12:41.7641870Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4'
2025-03-14T04:12:41.7810675Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446'
2025-03-14T04:12:41.8200293Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850'
2025-03-14T04:12:41.9101672Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5'
2025-03-14T04:12:41.9248369Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150'
2025-03-14T04:12:41.9604634Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164'
2025-03-14T04:12:42.0005225Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347'
2025-03-14T04:12:42.0450217Z Submodule path 'third_party/kleidiai': checked out 'ef685a13cfbe8d418aa2ed34350e21e4938358b6'
2025-03-14T04:12:42.0785601Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78'
2025-03-14T04:12:42.1726658Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806'
2025-03-14T04:12:42.4650473Z Submodule path 'third_party/onnx': checked out 'b8baa8446686496da4cc8fda09f2b6fe65c2a02c'
2025-03-14T04:12:42.4980894Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '3e9dfa2866941655c56877882565e7577de6fc7b'
2025-03-14T04:12:42.5605350Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878'
2025-03-14T04:12:42.5792536Z Submodule path 'third_party/opentelemetry-cpp/third_party/benchmark': checked out 'd572f4777349d43653b21d6c2fc63020ab326db2'
2025-03-14T04:12:42.6146841Z Submodule path 'third_party/opentelemetry-cpp/third_party/googletest': checked out 'b796f7d44681514f58a683a3a71ff17c94edb0c1'
2025-03-14T04:12:42.6258184Z Submodule path 'third_party/opentelemetry-cpp/third_party/ms-gsl': checked out '6f4529395c5b7c2d661812257cd6780c67e54afa'
2025-03-14T04:12:42.7198423Z Submodule path 'third_party/opentelemetry-cpp/third_party/nlohmann-json': checked out 'bc889afb4c5bf1c0d8ee29ef35eaaf4c8bef8a5d'
2025-03-14T04:12:42.7333372Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto': checked out '4ca4f0335c63cda7ab31ea7ed70d6553aee14dce'
2025-03-14T04:12:42.7469425Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp': checked out '06b57f48ded1fa3bdd3d4346f6ef29e40e08eaf5'
2025-03-14T04:12:42.7612896Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp': checked out 'c9ffcdda9086ffd9e1283ea7a0276d831f3c8a8d'
2025-03-14T04:12:42.9825272Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb': checked out 'eefb26f82b233268fc98577d265352720d477ba4'
2025-03-14T04:12:43.0227638Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929'
2025-03-14T04:12:43.4135016Z Submodule path 'third_party/opentelemetry-cpp/tools/vcpkg': checked out '8eb57355a4ffb410a2e94c07b4dca2dffbee8e50'
2025-03-14T04:12:43.4233960Z Submodule path 'third_party/pocketfft': checked out '9d3ab05a7fffbc71a492bc6a17be034e83e8f0fe'
2025-03-14T04:12:43.6569683Z Submodule path 'third_party/protobuf': checked out 'd1eca4e4b421cd2997495c4b4e65cea6be4e9b8a'
2025-03-14T04:12:43.6707269Z Submodule path 'third_party/protobuf/third_party/benchmark': checked out '5b7683f49e1e9223cf9927b24f6fd3d6bd82e3f8'
2025-03-14T04:12:43.7138639Z Submodule path 'third_party/protobuf/third_party/googletest': checked out '5ec7f0c4a113e2f18ac2c6cc7df51ad6afc24081'
2025-03-14T04:12:43.7218331Z Submodule path 'third_party/psimd': checked out '072586a71b55b7f8c584153d223e95687148a900'
2025-03-14T04:12:43.7336012Z Submodule path 'third_party/pthreadpool': checked out '4fe0e1e183925bf8cfa6aae24237e724a96479b8'
2025-03-14T04:12:43.7636834Z Submodule path 'third_party/pybind11': checked out 'a2e59f0e7065404b44dfe92a28aca47ba1378dc4'
2025-03-14T04:12:43.7885462Z Submodule path 'third_party/python-peachpy': checked out 'f45429b087dd7d5bc78bb40dc7cf06425c252d67'
2025-03-14T04:12:43.8253936Z Submodule path 'third_party/sleef': checked out '56e1f79cb140fb9326d612d0be06b5250565cade'
2025-03-14T04:12:43.8495223Z Submodule path 'third_party/tensorpipe': checked out '52791a2fd214b2a9dc5759d36725909c1daa7f2e'
2025-03-14T04:12:43.8883064Z Submodule path 'third_party/tensorpipe/third_party/googletest': checked out 'aee0f9d9b5b87796ee8a0ab26b7587ec30e8858e'
2025-03-14T04:12:43.9020712Z Submodule path 'third_party/tensorpipe/third_party/libnop': checked out '910b55815be16109f04f4180e9adee14fb4ce281'
2025-03-14T04:12:43.9570343Z Submodule path 'third_party/tensorpipe/third_party/libuv': checked out '1dff88e5161cba5c59276d2070d2e304e4dcb242'
2025-03-14T04:12:43.9823246Z Submodule path 'third_party/tensorpipe/third_party/pybind11': checked out 'a23996fce38ff6ccfbcdc09f1e63f2c4be5ea2ef'
2025-03-14T04:12:43.9903799Z Submodule path 'third_party/tensorpipe/third_party/pybind11/tools/clang': checked out '6a00cbc4a9b8e68b71caf7f774b3f9c753ae84d5'
2025-03-14T04:12:43.9945935Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0
2025-03-14T04:12:44.0234346Z Entering 'android/libs/fbjni'
2025-03-14T04:12:44.0272213Z Entering 'third_party/FP16'
2025-03-14T04:12:44.0311429Z Entering 'third_party/FXdiv'
2025-03-14T04:12:44.0345078Z Entering 'third_party/NNPACK'
2025-03-14T04:12:44.0384885Z Entering 'third_party/NVTX'
2025-03-14T04:12:44.0427465Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:12:44.0459261Z Entering 'third_party/XNNPACK'
2025-03-14T04:12:44.0512716Z Entering 'third_party/benchmark'
2025-03-14T04:12:44.0547747Z Entering 'third_party/composable_kernel'
2025-03-14T04:12:44.0592874Z Entering 'third_party/cpp-httplib'
2025-03-14T04:12:44.0633020Z Entering 'third_party/cpuinfo'
2025-03-14T04:12:44.0660292Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:12:44.0704195Z Entering 'third_party/cutlass'
2025-03-14T04:12:44.0748909Z Entering 'third_party/eigen'
2025-03-14T04:12:44.0789112Z Entering 'third_party/fbgemm'
2025-03-14T04:12:44.0821770Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:12:44.0853085Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:12:44.0892241Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:12:44.0928630Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:12:44.0962385Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:12:44.0997500Z Entering 'third_party/flash-attention'
2025-03-14T04:12:44.1033964Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:12:44.1075424Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:12:44.1117440Z Entering 'third_party/flatbuffers'
2025-03-14T04:12:44.1154364Z Entering 'third_party/fmt'
2025-03-14T04:12:44.1190952Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:12:44.1227444Z Entering 'third_party/gloo'
2025-03-14T04:12:44.1260409Z Entering 'third_party/googletest'
2025-03-14T04:12:44.1301401Z Entering 'third_party/ideep'
2025-03-14T04:12:44.1337744Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:12:44.1383190Z Entering 'third_party/ittapi'
2025-03-14T04:12:44.1423889Z Entering 'third_party/kineto'
2025-03-14T04:12:44.1455952Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:12:44.1495016Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:12:44.1531437Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:12:44.1571191Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:12:44.1602207Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:12:44.1638229Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:12:44.1674766Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:12:44.1711693Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:12:44.1748999Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:12:44.1791651Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:12:44.1835587Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:12:44.1871874Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:12:44.1912285Z Entering 'third_party/kleidiai'
2025-03-14T04:12:44.1957264Z Entering 'third_party/mimalloc'
2025-03-14T04:12:44.1998709Z Entering 'third_party/nlohmann'
2025-03-14T04:12:44.2040578Z Entering 'third_party/onnx'
2025-03-14T04:12:44.2082478Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:12:44.2119595Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:12:44.2162148Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:12:44.2204034Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:12:44.2237235Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:12:44.2277228Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:12:44.2313208Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:12:44.2348019Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:12:44.2384307Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:12:44.2422355Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:12:44.2477208Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:12:44.2502444Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:12:44.2553400Z Entering 'third_party/pocketfft'
2025-03-14T04:12:44.2595836Z Entering 'third_party/protobuf'
2025-03-14T04:12:44.2633420Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:12:44.2670920Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:12:44.2709013Z Entering 'third_party/psimd'
2025-03-14T04:12:44.2746034Z Entering 'third_party/pthreadpool'
2025-03-14T04:12:44.2790046Z Entering 'third_party/pybind11'
2025-03-14T04:12:44.2824908Z Entering 'third_party/python-peachpy'
2025-03-14T04:12:44.2861138Z Entering 'third_party/sleef'
2025-03-14T04:12:44.2896167Z Entering 'third_party/tensorpipe'
2025-03-14T04:12:44.2932047Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:12:44.2971870Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:12:44.3008478Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:12:44.3046978Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:12:44.3082987Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:12:44.3138817Z ##[endgroup]
2025-03-14T04:12:44.3144620Z ##[group]Persisting credentials for submodules
2025-03-14T04:12:44.3146355Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'url\.https\:\/\/github\.com\/\.insteadOf' && git config --local --unset-all 'url.https://github.com/.insteadOf' || :"
2025-03-14T04:12:44.3429591Z Entering 'android/libs/fbjni'
2025-03-14T04:12:44.3456800Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3457070Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3491030Z Entering 'third_party/FP16'
2025-03-14T04:12:44.3524286Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3526400Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3556267Z Entering 'third_party/FXdiv'
2025-03-14T04:12:44.3589595Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3593633Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3624286Z Entering 'third_party/NNPACK'
2025-03-14T04:12:44.3651567Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3651991Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3685361Z Entering 'third_party/NVTX'
2025-03-14T04:12:44.3713505Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3713825Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3747276Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:12:44.3782869Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3783343Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3813082Z Entering 'third_party/XNNPACK'
2025-03-14T04:12:44.3843831Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3844112Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3885937Z Entering 'third_party/benchmark'
2025-03-14T04:12:44.3918756Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3920288Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3952279Z Entering 'third_party/composable_kernel'
2025-03-14T04:12:44.3984010Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3984478Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4022096Z Entering 'third_party/cpp-httplib'
2025-03-14T04:12:44.4052991Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4054874Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4089693Z Entering 'third_party/cpuinfo'
2025-03-14T04:12:44.4118227Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4121801Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4152291Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:12:44.4183458Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4187653Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4216798Z Entering 'third_party/cutlass'
2025-03-14T04:12:44.4246633Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4247029Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4289722Z Entering 'third_party/eigen'
2025-03-14T04:12:44.4322626Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4324560Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4357655Z Entering 'third_party/fbgemm'
2025-03-14T04:12:44.4391968Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4393710Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4426851Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:12:44.4454503Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4454931Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4490814Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:12:44.4519640Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4521462Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4556802Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:12:44.4580246Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4580668Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4620051Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:12:44.4649473Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4649777Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4681367Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:12:44.4711477Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4713384Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4748504Z Entering 'third_party/flash-attention'
2025-03-14T04:12:44.4782525Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4785273Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4813082Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:12:44.4841713Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4843662Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4878818Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:12:44.4911476Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4914940Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4953371Z Entering 'third_party/flatbuffers'
2025-03-14T04:12:44.4985602Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4989043Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5021203Z Entering 'third_party/fmt'
2025-03-14T04:12:44.5049515Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5051285Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5085588Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:12:44.5114829Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5117067Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5149947Z Entering 'third_party/gloo'
2025-03-14T04:12:44.5179373Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5182870Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5217494Z Entering 'third_party/googletest'
2025-03-14T04:12:44.5242162Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5243797Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5277320Z Entering 'third_party/ideep'
2025-03-14T04:12:44.5306496Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5308445Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5340029Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:12:44.5370561Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5371156Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5412142Z Entering 'third_party/ittapi'
2025-03-14T04:12:44.5443062Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5444742Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5476077Z Entering 'third_party/kineto'
2025-03-14T04:12:44.5504872Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5506884Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5538120Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:12:44.5571043Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5571959Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5602943Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:12:44.5630000Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5631636Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5673375Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:12:44.5703323Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5705305Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5738581Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:12:44.5769401Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5771508Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5806075Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:12:44.5836643Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5836940Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5870547Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:12:44.5899438Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5904143Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5934422Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:12:44.5962282Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5967038Z url.https://github.com/.insteadof
2025-03-14T04:12:44.5996777Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:12:44.6026820Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6027258Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6058299Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:12:44.6090960Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6091504Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6128299Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:12:44.6155239Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6155678Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6204861Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:12:44.6232592Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6236712Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6263999Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:12:44.6295164Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6295591Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6329273Z Entering 'third_party/kleidiai'
2025-03-14T04:12:44.6377454Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6377917Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6442487Z Entering 'third_party/mimalloc'
2025-03-14T04:12:44.6473956Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6474412Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6505342Z Entering 'third_party/nlohmann'
2025-03-14T04:12:44.6536244Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6536682Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6570598Z Entering 'third_party/onnx'
2025-03-14T04:12:44.6600713Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6601517Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6647393Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:12:44.6673008Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6677781Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6708667Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:12:44.6741518Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6746407Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6769336Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:12:44.6802470Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6802942Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6837417Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:12:44.6868552Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6870542Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6902860Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:12:44.6932041Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6936892Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6965670Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:12:44.6993006Z url.https://github.com/.insteadof
2025-03-14T04:12:44.6993429Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7032932Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:12:44.7065997Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7066276Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7103590Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:12:44.7132757Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7138029Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7165561Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:12:44.7192365Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7196717Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7228119Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:12:44.7252996Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7253281Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7292672Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:12:44.7323242Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7323673Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7361716Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:12:44.7391649Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7392088Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7445162Z Entering 'third_party/pocketfft'
2025-03-14T04:12:44.7472552Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7472980Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7508183Z Entering 'third_party/protobuf'
2025-03-14T04:12:44.7540024Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7540454Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7589671Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:12:44.7600553Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7600976Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7635296Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:12:44.7659791Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7660078Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7767907Z Entering 'third_party/psimd'
2025-03-14T04:12:44.7795263Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7795681Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7833237Z Entering 'third_party/pthreadpool'
2025-03-14T04:12:44.7861869Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7865633Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7892371Z Entering 'third_party/pybind11'
2025-03-14T04:12:44.7919468Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7919861Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7957433Z Entering 'third_party/python-peachpy'
2025-03-14T04:12:44.7985332Z url.https://github.com/.insteadof
2025-03-14T04:12:44.7995209Z url.https://github.com/.insteadof
2025-03-14T04:12:44.8018273Z Entering 'third_party/sleef'
2025-03-14T04:12:44.8049997Z url.https://github.com/.insteadof
2025-03-14T04:12:44.8050445Z url.https://github.com/.insteadof
2025-03-14T04:12:44.8079413Z Entering 'third_party/tensorpipe'
2025-03-14T04:12:44.8109759Z url.https://github.com/.insteadof
2025-03-14T04:12:44.8110156Z url.https://github.com/.insteadof
2025-03-14T04:12:44.8144116Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:12:44.8171909Z url.https://github.com/.insteadof
2025-03-14T04:12:44.8172351Z url.https://github.com/.insteadof
2025-03-14T04:12:44.8212245Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:12:44.8239641Z url.https://github.com/.insteadof
2025-03-14T04:12:44.8240078Z url.https://github.com/.insteadof
2025-03-14T04:12:44.8272318Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:12:44.8299875Z url.https://github.com/.insteadof
2025-03-14T04:12:44.8304685Z url.https://github.com/.insteadof
2025-03-14T04:12:44.8332705Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:12:44.8362970Z url.https://github.com/.insteadof
2025-03-14T04:12:44.8363384Z url.https://github.com/.insteadof
2025-03-14T04:12:44.8401006Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:12:44.8434247Z url.https://github.com/.insteadof
2025-03-14T04:12:44.8436372Z url.https://github.com/.insteadof
2025-03-14T04:12:44.8490282Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url"
2025-03-14T04:12:44.8790309Z Entering 'android/libs/fbjni'
2025-03-14T04:12:44.8830258Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config	remote.origin.url
2025-03-14T04:12:44.8847146Z Entering 'third_party/FP16'
2025-03-14T04:12:44.8888416Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config	remote.origin.url
2025-03-14T04:12:44.8907500Z Entering 'third_party/FXdiv'
2025-03-14T04:12:44.8948192Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config	remote.origin.url
2025-03-14T04:12:44.8958860Z Entering 'third_party/NNPACK'
2025-03-14T04:12:44.9008910Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK/config	remote.origin.url
2025-03-14T04:12:44.9027577Z Entering 'third_party/NVTX'
2025-03-14T04:12:44.9068130Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NVTX/config	remote.origin.url
2025-03-14T04:12:44.9084205Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:12:44.9125347Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/VulkanMemoryAllocator/config	remote.origin.url
2025-03-14T04:12:44.9143654Z Entering 'third_party/XNNPACK'
2025-03-14T04:12:44.9185235Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/XNNPACK/config	remote.origin.url
2025-03-14T04:12:44.9213541Z Entering 'third_party/benchmark'
2025-03-14T04:12:44.9253724Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/benchmark/config	remote.origin.url
2025-03-14T04:12:44.9276749Z Entering 'third_party/composable_kernel'
2025-03-14T04:12:44.9311591Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/composable_kernel/config	remote.origin.url
2025-03-14T04:12:44.9334077Z Entering 'third_party/cpp-httplib'
2025-03-14T04:12:44.9379291Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpp-httplib/config	remote.origin.url
2025-03-14T04:12:44.9413749Z Entering 'third_party/cpuinfo'
2025-03-14T04:12:44.9438336Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config	remote.origin.url
2025-03-14T04:12:44.9457428Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:12:44.9503985Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config	remote.origin.url
2025-03-14T04:12:44.9523324Z Entering 'third_party/cutlass'
2025-03-14T04:12:44.9560407Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config	remote.origin.url
2025-03-14T04:12:44.9584496Z Entering 'third_party/eigen'
2025-03-14T04:12:44.9630354Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/eigen/config	remote.origin.url
2025-03-14T04:12:44.9647301Z Entering 'third_party/fbgemm'
2025-03-14T04:12:44.9694319Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config	remote.origin.url
2025-03-14T04:12:44.9707135Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:12:44.9745903Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/asmjit/config	remote.origin.url
2025-03-14T04:12:44.9757676Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:12:44.9804293Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cpuinfo/config	remote.origin.url
2025-03-14T04:12:44.9829225Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:12:44.9871679Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cutlass/config	remote.origin.url
2025-03-14T04:12:44.9893668Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:12:44.9935475Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:12:44.9949021Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:12:44.9994901Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/hipify_torch/config	remote.origin.url
2025-03-14T04:12:45.0012688Z Entering 'third_party/flash-attention'
2025-03-14T04:12:45.0054287Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/config	remote.origin.url
2025-03-14T04:12:45.0072208Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:12:45.0114482Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/modules/csrc/composable_kernel/config	remote.origin.url
2025-03-14T04:12:45.0136905Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:12:45.0181842Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/modules/csrc/cutlass/config	remote.origin.url
2025-03-14T04:12:45.0207211Z Entering 'third_party/flatbuffers'
2025-03-14T04:12:45.0247688Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flatbuffers/config	remote.origin.url
2025-03-14T04:12:45.0267995Z Entering 'third_party/fmt'
2025-03-14T04:12:45.0306444Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fmt/config	remote.origin.url
2025-03-14T04:12:45.0327075Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:12:45.0368114Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gemmlowp/gemmlowp/config	remote.origin.url
2025-03-14T04:12:45.0385675Z Entering 'third_party/gloo'
2025-03-14T04:12:45.0425045Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config	remote.origin.url
2025-03-14T04:12:45.0444257Z Entering 'third_party/googletest'
2025-03-14T04:12:45.0491809Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:12:45.0507209Z Entering 'third_party/ideep'
2025-03-14T04:12:45.0550767Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config	remote.origin.url
2025-03-14T04:12:45.0562303Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:12:45.0608080Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config	remote.origin.url
2025-03-14T04:12:45.0628840Z Entering 'third_party/ittapi'
2025-03-14T04:12:45.0674726Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config	remote.origin.url
2025-03-14T04:12:45.0687134Z Entering 'third_party/kineto'
2025-03-14T04:12:45.0732523Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/config	remote.origin.url
2025-03-14T04:12:45.0748280Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:12:45.0790199Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/config	remote.origin.url
2025-03-14T04:12:45.0803076Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:12:45.0845613Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/DCGM/config	remote.origin.url
2025-03-14T04:12:45.0861955Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:12:45.0912001Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/cpr/config	remote.origin.url
2025-03-14T04:12:45.0925542Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:12:45.0967866Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/fmt/config	remote.origin.url
2025-03-14T04:12:45.0982456Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:12:45.1024602Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/config	remote.origin.url
2025-03-14T04:12:45.1039665Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:12:45.1080115Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/modules/doc/config	remote.origin.url
2025-03-14T04:12:45.1092544Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:12:45.1137541Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/glog/config	remote.origin.url
2025-03-14T04:12:45.1167778Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:12:45.1192625Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:12:45.1211285Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:12:45.1254598Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/json/config	remote.origin.url
2025-03-14T04:12:45.1276664Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:12:45.1317754Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/pfs/config	remote.origin.url
2025-03-14T04:12:45.1336800Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:12:45.1379016Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/fmt/config	remote.origin.url
2025-03-14T04:12:45.1395380Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:12:45.1438026Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/googletest/config	remote.origin.url
2025-03-14T04:12:45.1455183Z Entering 'third_party/kleidiai'
2025-03-14T04:12:45.1500038Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kleidiai/config	remote.origin.url
2025-03-14T04:12:45.1515726Z Entering 'third_party/mimalloc'
2025-03-14T04:12:45.1559264Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/mimalloc/config	remote.origin.url
2025-03-14T04:12:45.1580438Z Entering 'third_party/nlohmann'
2025-03-14T04:12:45.1621874Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config	remote.origin.url
2025-03-14T04:12:45.1653184Z Entering 'third_party/onnx'
2025-03-14T04:12:45.1681473Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config	remote.origin.url
2025-03-14T04:12:45.1708942Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:12:45.1748996Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/pybind11/config	remote.origin.url
2025-03-14T04:12:45.1767191Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:12:45.1811553Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/config	remote.origin.url
2025-03-14T04:12:45.1827235Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:12:45.1877626Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/benchmark/config	remote.origin.url
2025-03-14T04:12:45.1885001Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:12:45.1926580Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:12:45.1942981Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:12:45.1983676Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/ms-gsl/config	remote.origin.url
2025-03-14T04:12:45.2003098Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:12:45.2044216Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/nlohmann-json/config	remote.origin.url
2025-03-14T04:12:45.2057511Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:12:45.2104602Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentelemetry-proto/config	remote.origin.url
2025-03-14T04:12:45.2121277Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:12:45.2164652Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentracing-cpp/config	remote.origin.url
2025-03-14T04:12:45.2183759Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:12:45.2232443Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/config	remote.origin.url
2025-03-14T04:12:45.2245738Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:12:45.2288817Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/civetweb/config	remote.origin.url
2025-03-14T04:12:45.2328914Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:12:45.2351272Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/googletest/config	remote.origin.url
2025-03-14T04:12:45.2370222Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:12:45.2413411Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/tools/vcpkg/config	remote.origin.url
2025-03-14T04:12:45.2446567Z Entering 'third_party/pocketfft'
2025-03-14T04:12:45.2489671Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config	remote.origin.url
2025-03-14T04:12:45.2503512Z Entering 'third_party/protobuf'
2025-03-14T04:12:45.2544777Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config	remote.origin.url
2025-03-14T04:12:45.2561655Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:12:45.2609209Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config	remote.origin.url
2025-03-14T04:12:45.2625795Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:12:45.2667336Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:12:45.2685903Z Entering 'third_party/psimd'
2025-03-14T04:12:45.2727897Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config	remote.origin.url
2025-03-14T04:12:45.2744824Z Entering 'third_party/pthreadpool'
2025-03-14T04:12:45.2785452Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config	remote.origin.url
2025-03-14T04:12:45.2802912Z Entering 'third_party/pybind11'
2025-03-14T04:12:45.2845302Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config	remote.origin.url
2025-03-14T04:12:45.2858220Z Entering 'third_party/python-peachpy'
2025-03-14T04:12:45.2904933Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config	remote.origin.url
2025-03-14T04:12:45.2916326Z Entering 'third_party/sleef'
2025-03-14T04:12:45.2963177Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config	remote.origin.url
2025-03-14T04:12:45.2974646Z Entering 'third_party/tensorpipe'
2025-03-14T04:12:45.3018931Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config	remote.origin.url
2025-03-14T04:12:45.3032790Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:12:45.3074015Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:12:45.3089647Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:12:45.3128411Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config	remote.origin.url
2025-03-14T04:12:45.3147274Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:12:45.3190277Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config	remote.origin.url
2025-03-14T04:12:45.3204659Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:12:45.3248526Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config	remote.origin.url
2025-03-14T04:12:45.3260879Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:12:45.3303499Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/modules/tools/clang/config	remote.origin.url
2025-03-14T04:12:45.4411216Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:'
2025-03-14T04:12:45.4698441Z Entering 'android/libs/fbjni'
2025-03-14T04:12:45.4730642Z Entering 'third_party/FP16'
2025-03-14T04:12:45.4769775Z Entering 'third_party/FXdiv'
2025-03-14T04:12:45.4809799Z Entering 'third_party/NNPACK'
2025-03-14T04:12:45.4849675Z Entering 'third_party/NVTX'
2025-03-14T04:12:45.4885957Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:12:45.4926037Z Entering 'third_party/XNNPACK'
2025-03-14T04:12:45.4974305Z Entering 'third_party/benchmark'
2025-03-14T04:12:45.5012009Z Entering 'third_party/composable_kernel'
2025-03-14T04:12:45.5054295Z Entering 'third_party/cpp-httplib'
2025-03-14T04:12:45.5093470Z Entering 'third_party/cpuinfo'
2025-03-14T04:12:45.5131780Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:12:45.5166891Z Entering 'third_party/cutlass'
2025-03-14T04:12:45.5210662Z Entering 'third_party/eigen'
2025-03-14T04:12:45.5245726Z Entering 'third_party/fbgemm'
2025-03-14T04:12:45.5282180Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:12:45.5316801Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:12:45.5351477Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:12:45.5387545Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:12:45.5424357Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:12:45.5457849Z Entering 'third_party/flash-attention'
2025-03-14T04:12:45.5500194Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:12:45.5544573Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:12:45.5592947Z Entering 'third_party/flatbuffers'
2025-03-14T04:12:45.5627774Z Entering 'third_party/fmt'
2025-03-14T04:12:45.5659741Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:12:45.5698479Z Entering 'third_party/gloo'
2025-03-14T04:12:45.5736048Z Entering 'third_party/googletest'
2025-03-14T04:12:45.5769320Z Entering 'third_party/ideep'
2025-03-14T04:12:45.5806115Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:12:45.5849262Z Entering 'third_party/ittapi'
2025-03-14T04:12:45.5883516Z Entering 'third_party/kineto'
2025-03-14T04:12:45.5921518Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:12:45.5963793Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:12:45.6007234Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:12:45.6040417Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:12:45.6077190Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:12:45.6115295Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:12:45.6155542Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:12:45.6193392Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:12:45.6236265Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:12:45.6273239Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:12:45.6310670Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:12:45.6342792Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:12:45.6385102Z Entering 'third_party/kleidiai'
2025-03-14T04:12:45.6424946Z Entering 'third_party/mimalloc'
2025-03-14T04:12:45.6461359Z Entering 'third_party/nlohmann'
2025-03-14T04:12:45.6502539Z Entering 'third_party/onnx'
2025-03-14T04:12:45.6553945Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:12:45.6611784Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:12:45.6631837Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:12:45.6667096Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:12:45.6699983Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:12:45.6732656Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:12:45.6772066Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:12:45.6806100Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:12:45.6841218Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:12:45.6874666Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:12:45.6912622Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:12:45.6950946Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:12:45.7002066Z Entering 'third_party/pocketfft'
2025-03-14T04:12:45.7044817Z Entering 'third_party/protobuf'
2025-03-14T04:12:45.7083948Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:12:45.7119793Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:12:45.7157745Z Entering 'third_party/psimd'
2025-03-14T04:12:45.7193119Z Entering 'third_party/pthreadpool'
2025-03-14T04:12:45.7229274Z Entering 'third_party/pybind11'
2025-03-14T04:12:45.7266258Z Entering 'third_party/python-peachpy'
2025-03-14T04:12:45.7303147Z Entering 'third_party/sleef'
2025-03-14T04:12:45.7336984Z Entering 'third_party/tensorpipe'
2025-03-14T04:12:45.7377738Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:12:45.7411993Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:12:45.7452625Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:12:45.7500280Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:12:45.7535473Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:12:45.7597199Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:'
2025-03-14T04:12:45.7878756Z Entering 'android/libs/fbjni'
2025-03-14T04:12:45.7919245Z Entering 'third_party/FP16'
2025-03-14T04:12:45.7952601Z Entering 'third_party/FXdiv'
2025-03-14T04:12:45.7996847Z Entering 'third_party/NNPACK'
2025-03-14T04:12:45.8034153Z Entering 'third_party/NVTX'
2025-03-14T04:12:45.8071392Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:12:45.8109499Z Entering 'third_party/XNNPACK'
2025-03-14T04:12:45.8158479Z Entering 'third_party/benchmark'
2025-03-14T04:12:45.8195300Z Entering 'third_party/composable_kernel'
2025-03-14T04:12:45.8238853Z Entering 'third_party/cpp-httplib'
2025-03-14T04:12:45.8276055Z Entering 'third_party/cpuinfo'
2025-03-14T04:12:45.8312871Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:12:45.8349457Z Entering 'third_party/cutlass'
2025-03-14T04:12:45.8397706Z Entering 'third_party/eigen'
2025-03-14T04:12:45.8436299Z Entering 'third_party/fbgemm'
2025-03-14T04:12:45.8475361Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:12:45.8511557Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:12:45.8544322Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:12:45.8587777Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:12:45.8623645Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:12:45.8662096Z Entering 'third_party/flash-attention'
2025-03-14T04:12:45.8699105Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:12:45.8739567Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:12:45.8785686Z Entering 'third_party/flatbuffers'
2025-03-14T04:12:45.8826648Z Entering 'third_party/fmt'
2025-03-14T04:12:45.8869079Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:12:45.8908278Z Entering 'third_party/gloo'
2025-03-14T04:12:45.8946952Z Entering 'third_party/googletest'
2025-03-14T04:12:45.8982247Z Entering 'third_party/ideep'
2025-03-14T04:12:45.9018000Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:12:45.9057630Z Entering 'third_party/ittapi'
2025-03-14T04:12:45.9092929Z Entering 'third_party/kineto'
2025-03-14T04:12:45.9129219Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:12:45.9167145Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:12:45.9209030Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:12:45.9244345Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:12:45.9279668Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:12:45.9313312Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:12:45.9372733Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:12:45.9403119Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:12:45.9439163Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:12:45.9473502Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:12:45.9513471Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:12:45.9547353Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:12:45.9589130Z Entering 'third_party/kleidiai'
2025-03-14T04:12:45.9627235Z Entering 'third_party/mimalloc'
2025-03-14T04:12:45.9661250Z Entering 'third_party/nlohmann'
2025-03-14T04:12:45.9699074Z Entering 'third_party/onnx'
2025-03-14T04:12:45.9746739Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:12:45.9786740Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:12:45.9832099Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:12:45.9857860Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:12:45.9892005Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:12:45.9927213Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:12:45.9958989Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:12:45.9996119Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:12:46.0031691Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:12:46.0065645Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:12:46.0102421Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:12:46.0143431Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:12:46.0202848Z Entering 'third_party/pocketfft'
2025-03-14T04:12:46.0238236Z Entering 'third_party/protobuf'
2025-03-14T04:12:46.0275170Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:12:46.0321751Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:12:46.0357899Z Entering 'third_party/psimd'
2025-03-14T04:12:46.0397906Z Entering 'third_party/pthreadpool'
2025-03-14T04:12:46.0433573Z Entering 'third_party/pybind11'
2025-03-14T04:12:46.0472516Z Entering 'third_party/python-peachpy'
2025-03-14T04:12:46.0502633Z Entering 'third_party/sleef'
2025-03-14T04:12:46.0541017Z Entering 'third_party/tensorpipe'
2025-03-14T04:12:46.0577802Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:12:46.0610049Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:12:46.0649739Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:12:46.0683393Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:12:46.0715434Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:12:46.0772373Z ##[endgroup]
2025-03-14T04:12:46.0823583Z [command]/usr/bin/git log -1 --format=%H
2025-03-14T04:12:46.0847477Z aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:12:46.1005491Z Prepare all required actions
2025-03-14T04:12:46.1006015Z Getting action download info
2025-03-14T04:12:46.1956881Z ##[group]Run ./.github/actions/setup-linux
2025-03-14T04:12:46.1957121Z env:
2025-03-14T04:12:46.1957288Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:46.1957473Z ##[endgroup]
2025-03-14T04:12:46.1998892Z ##[group]Run set -euo pipefail
2025-03-14T04:12:46.1999169Z set -euo pipefail
2025-03-14T04:12:46.1999539Z function get_ec2_metadata() {
2025-03-14T04:12:46.1999806Z   # Pulled from instance metadata endpoint for EC2
2025-03-14T04:12:46.2000224Z   # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html
2025-03-14T04:12:46.2000583Z   category=$1
2025-03-14T04:12:46.2000835Z   # If it is GCP runner (runner name contains gcp), do not run this
2025-03-14T04:12:46.2001128Z   runner_name_str=i-00f46405241e0bbc9
2025-03-14T04:12:46.2001411Z   if [[ -f /.inarc ]]; then
2025-03-14T04:12:46.2001655Z     echo "ARC Runner, no info on ec2 metadata"
2025-03-14T04:12:46.2001918Z   elif [[ $runner_name_str == *"gcp"* ]]; then
2025-03-14T04:12:46.2002219Z     echo "Runner is from Google Cloud Platform, No info on ec2 metadata"
2025-03-14T04:12:46.2002492Z   else
2025-03-14T04:12:46.2003019Z     curl -H "X-aws-ec2-metadata-token: $(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 30")" -fsSL "http://169.254.169.254/latest/meta-data/${category}"
2025-03-14T04:12:46.2003558Z   fi
2025-03-14T04:12:46.2003727Z }
2025-03-14T04:12:46.2003934Z echo "ami-id: $(get_ec2_metadata ami-id)"
2025-03-14T04:12:46.2004218Z echo "instance-id: $(get_ec2_metadata instance-id)"
2025-03-14T04:12:46.2004518Z echo "instance-type: $(get_ec2_metadata instance-type)"
2025-03-14T04:12:46.2004786Z echo "system info $(uname -a)"
2025-03-14T04:12:46.2009511Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:46.2009772Z env:
2025-03-14T04:12:46.2009945Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:46.2010137Z ##[endgroup]
2025-03-14T04:12:46.2135961Z ami-id: ami-08b5b3a93ed654d19
2025-03-14T04:12:46.2225687Z instance-id: i-00f46405241e0bbc9
2025-03-14T04:12:46.2306544Z instance-type: m7i-flex.8xlarge
2025-03-14T04:12:46.2315093Z system info Linux ip-10-0-63-215.ec2.internal 6.1.129-138.220.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Feb 25 22:18:43 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux
2025-03-14T04:12:46.2340865Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:12:46.2341427Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:12:46.2345961Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:46.2346235Z env:
2025-03-14T04:12:46.2346411Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:46.2346605Z ##[endgroup]
2025-03-14T04:12:46.2396801Z ##[group]Run if systemctl is-active --quiet docker; then
2025-03-14T04:12:46.2397191Z if systemctl is-active --quiet docker; then
2025-03-14T04:12:46.2397514Z     echo "Docker daemon is running...";
2025-03-14T04:12:46.2397824Z else
2025-03-14T04:12:46.2398128Z     echo "Starting docker deamon..." && sudo systemctl start docker;
2025-03-14T04:12:46.2398465Z fi
2025-03-14T04:12:46.2402927Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:46.2403197Z env:
2025-03-14T04:12:46.2403397Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:46.2403658Z ##[endgroup]
2025-03-14T04:12:46.2522369Z Docker daemon is running...
2025-03-14T04:12:46.2567086Z ##[group]Run nick-fields/retry@v3.0.0
2025-03-14T04:12:46.2567401Z with:
2025-03-14T04:12:46.2567631Z   shell: bash
2025-03-14T04:12:46.2567974Z   timeout_minutes: 5
2025-03-14T04:12:46.2568241Z   max_attempts: 3
2025-03-14T04:12:46.2568502Z   retry_wait_seconds: 30
2025-03-14T04:12:46.2570012Z   command: AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\")
aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \
    --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com"

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

2025-03-14T04:12:46.2571643Z   polling_interval_seconds: 1
2025-03-14T04:12:46.2571953Z   warning_on_retry: true
2025-03-14T04:12:46.2572246Z   continue_on_error: false
2025-03-14T04:12:46.2572530Z env:
2025-03-14T04:12:46.2572790Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:46.2573036Z   AWS_RETRY_MODE: standard
2025-03-14T04:12:46.2573269Z   AWS_MAX_ATTEMPTS: 5
2025-03-14T04:12:46.2573535Z   AWS_DEFAULT_REGION: us-east-1
2025-03-14T04:12:46.2573800Z ##[endgroup]
2025-03-14T04:12:47.1511762Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-14T04:12:47.1512610Z Configure a credential helper to remove this warning. See
2025-03-14T04:12:47.1513122Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-14T04:12:47.1513455Z 
2025-03-14T04:12:47.1513543Z Login Succeeded
2025-03-14T04:12:47.3256252Z Command completed after 1 attempt(s).
2025-03-14T04:12:47.3319538Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-14T04:12:47.3319969Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-14T04:12:47.3320364Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-14T04:12:47.3325290Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:47.3325615Z env:
2025-03-14T04:12:47.3325839Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:47.3326098Z ##[endgroup]
2025-03-14T04:12:47.3411072Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2025-03-14T04:12:47.3411764Z # ignore expansion of "docker ps -q" since it could be empty
2025-03-14T04:12:47.3412185Z # shellcheck disable=SC2046
2025-03-14T04:12:47.3412507Z docker stop $(docker ps -q) || true
2025-03-14T04:12:47.3412823Z # Prune all of the docker images
2025-03-14T04:12:47.3413189Z docker system prune -af
2025-03-14T04:12:47.3417231Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:47.3418376Z env:
2025-03-14T04:12:47.3418671Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:47.3418959Z ##[endgroup]
2025-03-14T04:12:47.3681658Z "docker stop" requires at least 1 argument.
2025-03-14T04:12:47.3682031Z See 'docker stop --help'.
2025-03-14T04:12:47.3682397Z 
2025-03-14T04:12:47.3682602Z Usage:  docker stop [OPTIONS] CONTAINER [CONTAINER...]
2025-03-14T04:12:47.3682802Z 
2025-03-14T04:12:47.3682945Z Stop one or more running containers
2025-03-14T04:12:47.3842025Z Total reclaimed space: 0B
2025-03-14T04:12:47.3881806Z ##[group]Run set +e
2025-03-14T04:12:47.3882118Z set +e
2025-03-14T04:12:47.3882358Z set -x
2025-03-14T04:12:47.3882576Z 
2025-03-14T04:12:47.3882843Z PT_DOMAIN=download.pytorch.org
2025-03-14T04:12:47.3883302Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400,
2025-03-14T04:12:47.3883819Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last
2025-03-14T04:12:47.3884210Z # one is returned at random
2025-03-14T04:12:47.3884567Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1)
2025-03-14T04:12:47.3884879Z 
2025-03-14T04:12:47.3885228Z if [ -z "${RESOLVED_IP}" ]; then
2025-03-14T04:12:47.3885552Z   echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..."
2025-03-14T04:12:47.3885956Z   RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1)
2025-03-14T04:12:47.3886262Z 
2025-03-14T04:12:47.3886504Z   if [ -z "${RESOLVED_IP}" ]; then
2025-03-14T04:12:47.3886824Z     echo "Couldn't resolve ${PT_DOMAIN}, exiting..."
2025-03-14T04:12:47.3887195Z     exit 1
2025-03-14T04:12:47.3887460Z   fi
2025-03-14T04:12:47.3887709Z fi
2025-03-14T04:12:47.3887904Z 
2025-03-14T04:12:47.3888192Z if grep -r "${PT_DOMAIN}" /etc/hosts; then
2025-03-14T04:12:47.3888490Z   # Clean up any old records first
2025-03-14T04:12:47.3888791Z   sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts
2025-03-14T04:12:47.3889075Z fi
2025-03-14T04:12:47.3889286Z 
2025-03-14T04:12:47.3889582Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts
2025-03-14T04:12:47.3889889Z cat /etc/hosts
2025-03-14T04:12:47.3894794Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:47.3895134Z env:
2025-03-14T04:12:47.3895382Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:47.3895647Z ##[endgroup]
2025-03-14T04:12:47.3917428Z + PT_DOMAIN=download.pytorch.org
2025-03-14T04:12:47.3926584Z ++ dig -4 +short download.pytorch.org
2025-03-14T04:12:47.3927047Z ++ tail -n1
2025-03-14T04:12:47.4319219Z + RESOLVED_IP=18.160.10.22
2025-03-14T04:12:47.4319827Z + '[' -z 18.160.10.22 ']'
2025-03-14T04:12:47.4320329Z + grep -r download.pytorch.org /etc/hosts
2025-03-14T04:12:47.4329901Z 18.160.10.76 download.pytorch.org
2025-03-14T04:12:47.4330673Z + sudo sed -i /download.pytorch.org/d /etc/hosts
2025-03-14T04:12:47.6955502Z + echo '18.160.10.22 download.pytorch.org'
2025-03-14T04:12:47.6957015Z + sudo tee -a /etc/hosts
2025-03-14T04:12:47.7871172Z 18.160.10.22 download.pytorch.org
2025-03-14T04:12:47.7889184Z + cat /etc/hosts
2025-03-14T04:12:47.7898694Z 127.0.0.1   localhost localhost.localdomain localhost4 localhost4.localdomain4
2025-03-14T04:12:47.7904509Z ::1         localhost6 localhost6.localdomain6
2025-03-14T04:12:47.7904887Z 18.160.10.22 download.pytorch.org
2025-03-14T04:12:47.8024432Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main
2025-03-14T04:12:47.8024846Z with:
2025-03-14T04:12:47.8025390Z   docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.8025990Z   docker-build-dir: .ci/docker
2025-03-14T04:12:47.8026270Z   working-directory: .
2025-03-14T04:12:47.8026593Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:47.8026959Z   force-push: false
2025-03-14T04:12:47.8027187Z env:
2025-03-14T04:12:47.8027409Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:47.8027773Z ##[endgroup]
2025-03-14T04:12:47.8055143Z ##[group]Run set -ex
2025-03-14T04:12:47.8055460Z set -ex
2025-03-14T04:12:47.8055714Z 
2025-03-14T04:12:47.8056091Z # If the docker build directory or the build script doesn't exist, the action will
2025-03-14T04:12:47.8056707Z # gracefully return the docker image name as it is.  Pulling docker image in Linux
2025-03-14T04:12:47.8057195Z # job could then download the pre-built image as usual
2025-03-14T04:12:47.8057622Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then
2025-03-14T04:12:47.8058096Z   echo "skip=true" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.8058492Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.8058835Z 
2025-03-14T04:12:47.8059172Z   echo "There is no Docker build script in ${REPO_NAME} repo, skipping..."
2025-03-14T04:12:47.8059542Z   exit 0
2025-03-14T04:12:47.8059791Z else
2025-03-14T04:12:47.8060082Z   echo "skip=false" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.8060372Z fi
2025-03-14T04:12:47.8060868Z 
2025-03-14T04:12:47.8061384Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then
2025-03-14T04:12:47.8061940Z   # The docker image name already includes the ECR prefix and tag, so we can just
2025-03-14T04:12:47.8062367Z   # use it as it is, but first let's extract the tag
2025-03-14T04:12:47.8063004Z   DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}')
2025-03-14T04:12:47.8063416Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.8063828Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.8064146Z else
2025-03-14T04:12:47.8064411Z   DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}")
2025-03-14T04:12:47.8064792Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.8065229Z   echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.8065652Z fi
2025-03-14T04:12:47.8072486Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:47.8072808Z env:
2025-03-14T04:12:47.8073043Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:47.8073280Z   REPO_NAME: pytorch
2025-03-14T04:12:47.8073850Z   DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.8074409Z   DOCKER_BUILD_DIR: .ci/docker
2025-03-14T04:12:47.8074724Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:47.8075051Z ##[endgroup]
2025-03-14T04:12:47.8096899Z + [[ ! -d .ci/docker ]]
2025-03-14T04:12:47.8097261Z + [[ ! -f .ci/docker/build.sh ]]
2025-03-14T04:12:47.8097537Z + echo skip=false
2025-03-14T04:12:47.8098520Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849 == *\3\0\8\5\3\5\3\8\5\1\1\4\.\d\k\r\.\e\c\r\.\u\s\-\e\a\s\t\-\1\.\a\m\a\z\o\n\a\w\s\.\c\o\m\/\p\y\t\o\r\c\h* ]]
2025-03-14T04:12:47.8102753Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.8103443Z ++ awk -F '[:,]' '{print $2}'
2025-03-14T04:12:47.8124619Z + DOCKER_TAG=aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.8125132Z + echo docker-tag=aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.8126007Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.8157007Z ##[group]Run set +e
2025-03-14T04:12:47.8157425Z set +e
2025-03-14T04:12:47.8157650Z set -x
2025-03-14T04:12:47.8157911Z 
2025-03-14T04:12:47.8158134Z login() {
2025-03-14T04:12:47.8158534Z   aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1"
2025-03-14T04:12:47.8158936Z }
2025-03-14T04:12:47.8159163Z 
2025-03-14T04:12:47.8159398Z retry () {
2025-03-14T04:12:47.8159623Z   $*  || (sleep 1 && $*) || (sleep 2 && $*)
2025-03-14T04:12:47.8159900Z }
2025-03-14T04:12:47.8160220Z 
2025-03-14T04:12:47.8160451Z retry login "${DOCKER_REGISTRY}"
2025-03-14T04:12:47.8161003Z 
2025-03-14T04:12:47.8161264Z START_TIME=$(date +%s)
2025-03-14T04:12:47.8161549Z # Wait up to 120 minutes
2025-03-14T04:12:47.8161900Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do
2025-03-14T04:12:47.8162286Z   # Check if image already exists, if it does then skip building it
2025-03-14T04:12:47.8162675Z   if docker manifest inspect "${DOCKER_IMAGE}"; then
2025-03-14T04:12:47.8163006Z     exit 0
2025-03-14T04:12:47.8163238Z   fi
2025-03-14T04:12:47.8163645Z 
2025-03-14T04:12:47.8164010Z   # NB: This flag is used by Docker build workflow to push the image to ECR, so we can
2025-03-14T04:12:47.8164474Z   # use this to differentiate between the Docker build and regular build jobs. For the
2025-03-14T04:12:47.8164934Z   # latter, it will wait for the Docker images to become available before continuing
2025-03-14T04:12:47.8165440Z   if [ "${DOCKER_PUSH:-false}" == "true" ]; then
2025-03-14T04:12:47.8165772Z     # It's a Docker build job, let's build the image
2025-03-14T04:12:47.8166090Z     break
2025-03-14T04:12:47.8166331Z   else
2025-03-14T04:12:47.8166621Z     # It's a regular build job, wait for the image to become available
2025-03-14T04:12:47.8166987Z     sleep 300
2025-03-14T04:12:47.8167317Z   fi
2025-03-14T04:12:47.8167545Z done
2025-03-14T04:12:47.8167791Z 
2025-03-14T04:12:47.8168096Z # NB: This part requires a full checkout. Otherwise, the merge base will
2025-03-14T04:12:47.8168554Z # be empty.  The default action would be to continue rebuild the image
2025-03-14T04:12:47.8168949Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then
2025-03-14T04:12:47.8169304Z   # if we're on the base branch then use the parent commit
2025-03-14T04:12:47.8169675Z   MERGE_BASE=$(git rev-parse HEAD~)
2025-03-14T04:12:47.8169961Z else
2025-03-14T04:12:47.8170269Z   # otherwise we're on a PR, so use the most recent base commit
2025-03-14T04:12:47.8170648Z   MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION")
2025-03-14T04:12:47.8171012Z fi
2025-03-14T04:12:47.8171255Z 
2025-03-14T04:12:47.8202023Z if [[ -z "${MERGE_BASE}" ]]; then
2025-03-14T04:12:47.8202294Z   echo "rebuild=true" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.8202528Z 
2025-03-14T04:12:47.8202825Z   echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..."
2025-03-14T04:12:47.8203326Z   exit 0
2025-03-14T04:12:47.8203494Z fi
2025-03-14T04:12:47.8203640Z 
2025-03-14T04:12:47.8203847Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then
2025-03-14T04:12:47.8204250Z   echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit"
2025-03-14T04:12:47.8204595Z   exit 1
2025-03-14T04:12:47.8204751Z fi
2025-03-14T04:12:47.8204897Z 
2025-03-14T04:12:47.8205130Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}")
2025-03-14T04:12:47.8205523Z # If no image exists but the hash is the same as the previous hash then we should error out here
2025-03-14T04:12:47.8205877Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then
2025-03-14T04:12:47.8206282Z   echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch"
2025-03-14T04:12:47.8206732Z   echo "         Will re-build docker image to store in local cache, TTS may be longer"
2025-03-14T04:12:47.8207018Z fi
2025-03-14T04:12:47.8207170Z 
2025-03-14T04:12:47.8207356Z echo "rebuild=true" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.8211650Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:47.8211918Z env:
2025-03-14T04:12:47.8212098Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:47.8212341Z   DOCKER_BUILD_DIR: .ci/docker
2025-03-14T04:12:47.8212613Z   BASE_REVISION: aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:12:47.8213170Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.8213709Z   DOCKER_TAG: aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.8213998Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:47.8214256Z   DOCKER_PUSH: 
2025-03-14T04:12:47.8214433Z ##[endgroup]
2025-03-14T04:12:47.8234822Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:47.8235729Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:47.8236381Z + aws ecr get-login-password --region us-east-1
2025-03-14T04:12:47.8237380Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:48.2412654Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-14T04:12:48.2416707Z Configure a credential helper to remove this warning. See
2025-03-14T04:12:48.2416972Z Login Succeeded
2025-03-14T04:12:48.2417288Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-14T04:12:48.2417523Z 
2025-03-14T04:12:48.2436192Z ++ date +%s
2025-03-14T04:12:48.2443457Z + START_TIME=1741925568
2025-03-14T04:12:48.2443732Z ++ date +%s
2025-03-14T04:12:48.2451232Z + [[ 1741918368 -lt 1741925568 ]]
2025-03-14T04:12:48.2451941Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:48.4832684Z {
2025-03-14T04:12:48.4832980Z 	"schemaVersion": 2,
2025-03-14T04:12:48.4833532Z 	"mediaType": "application/vnd.docker.distribution.manifest.v2+json",
2025-03-14T04:12:48.4833942Z 	"config": {
2025-03-14T04:12:48.4834270Z 		"mediaType": "application/vnd.docker.container.image.v1+json",
2025-03-14T04:12:48.4834658Z 		"size": 42081,
2025-03-14T04:12:48.4835047Z 		"digest": "sha256:1bab961b3bd90ec6714167a095e5437a0239700971887d1c663377ef708f8526"
2025-03-14T04:12:48.4835469Z 	},
2025-03-14T04:12:48.4835676Z 	"layers": [
2025-03-14T04:12:48.4835881Z 		{
2025-03-14T04:12:48.4836201Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4836587Z 			"size": 30440118,
2025-03-14T04:12:48.4837013Z 			"digest": "sha256:8f84a9f2102e97a4a6bf673b150fc9894df5acc9618ad3484c6c36f768c1caa0"
2025-03-14T04:12:48.4837482Z 		},
2025-03-14T04:12:48.4838139Z 		{
2025-03-14T04:12:48.4838482Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4838885Z 			"size": 1896,
2025-03-14T04:12:48.4839321Z 			"digest": "sha256:f35880ae6ee6dd2a62a077d19ff6b5c4a69745027e986e8ed5fec074f76c859a"
2025-03-14T04:12:48.4839810Z 		},
2025-03-14T04:12:48.4840024Z 		{
2025-03-14T04:12:48.4840361Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4840785Z 			"size": 319404545,
2025-03-14T04:12:48.4841216Z 			"digest": "sha256:8eb502731466bf7440d89c4538928f5a175e8b54b788f1e8d194d0f864ff206d"
2025-03-14T04:12:48.4841677Z 		},
2025-03-14T04:12:48.4841898Z 		{
2025-03-14T04:12:48.4842251Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4842680Z 			"size": 864,
2025-03-14T04:12:48.4843119Z 			"digest": "sha256:d4ce6a1f04ff4b142de5bce2ef06e1538172f3f7245835a1393a2a3b87a616bb"
2025-03-14T04:12:48.4843608Z 		},
2025-03-14T04:12:48.4843823Z 		{
2025-03-14T04:12:48.4844185Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4844620Z 			"size": 106,
2025-03-14T04:12:48.4845062Z 			"digest": "sha256:ca18d6003a1855ff2be2115cc2154b50c52d2fef9f8628623f168c33016d3b33"
2025-03-14T04:12:48.4845545Z 		},
2025-03-14T04:12:48.4845757Z 		{
2025-03-14T04:12:48.4846118Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4846541Z 			"size": 704,
2025-03-14T04:12:48.4846982Z 			"digest": "sha256:f6b56646e2f02522c5afb19aff566ef53261a591ce3ecbeaa224ed40df1ed9a4"
2025-03-14T04:12:48.4847450Z 		},
2025-03-14T04:12:48.4847735Z 		{
2025-03-14T04:12:48.4848082Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4848480Z 			"size": 1216,
2025-03-14T04:12:48.4848917Z 			"digest": "sha256:454c9d6c7abb004ad3cf2a984a4c653d3dcb3af36d26bfe90988a24dfbe1650c"
2025-03-14T04:12:48.4849400Z 		},
2025-03-14T04:12:48.4849609Z 		{
2025-03-14T04:12:48.4849962Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4850420Z 			"size": 483,
2025-03-14T04:12:48.4850890Z 			"digest": "sha256:a66b8377f49fed837811a0ed9d612fc454b00944b0bafccbb4611b19e2fd2976"
2025-03-14T04:12:48.4851888Z 		},
2025-03-14T04:12:48.4852123Z 		{
2025-03-14T04:12:48.4852488Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4852921Z 			"size": 110341891,
2025-03-14T04:12:48.4853393Z 			"digest": "sha256:c75867b7d634b7bfdf1bb7a2cf5062bef03aa58f357db40fad0ca3db26633c1f"
2025-03-14T04:12:48.4853898Z 		},
2025-03-14T04:12:48.4854121Z 		{
2025-03-14T04:12:48.4854473Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4854938Z 			"size": 4165,
2025-03-14T04:12:48.4855410Z 			"digest": "sha256:64866485623d4cd2c086ae4dd8c332eba6200229a00c3457274adf9cf3878f44"
2025-03-14T04:12:48.4855927Z 		},
2025-03-14T04:12:48.4856152Z 		{
2025-03-14T04:12:48.4856559Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4857005Z 			"size": 1860,
2025-03-14T04:12:48.4857532Z 			"digest": "sha256:04b678046b3a28400a60c07993009616e6f2667c9052eeba35cd953db82b1af2"
2025-03-14T04:12:48.4858027Z 		},
2025-03-14T04:12:48.4858258Z 		{
2025-03-14T04:12:48.4858619Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4859058Z 			"size": 701,
2025-03-14T04:12:48.4859510Z 			"digest": "sha256:1e2892d1c0d6f1b67d3ba1d866912303eac78217715c2fa8631fd39c22f81996"
2025-03-14T04:12:48.4860009Z 		},
2025-03-14T04:12:48.4860219Z 		{
2025-03-14T04:12:48.4860770Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4861222Z 			"size": 477,
2025-03-14T04:12:48.4861680Z 			"digest": "sha256:564c8877ceb50a429fa53713805ae4726c040473188d1fb1e4dcd5ba9706304f"
2025-03-14T04:12:48.4862167Z 		},
2025-03-14T04:12:48.4862369Z 		{
2025-03-14T04:12:48.4862719Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4863425Z 			"size": 2831705826,
2025-03-14T04:12:48.4863915Z 			"digest": "sha256:8d7dafab91e2016111c6948bf0794c93e2721bf193c48886fe2240153b249e33"
2025-03-14T04:12:48.4864410Z 		},
2025-03-14T04:12:48.4864630Z 		{
2025-03-14T04:12:48.4864993Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4865562Z 			"size": 32,
2025-03-14T04:12:48.4866021Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-14T04:12:48.4866527Z 		},
2025-03-14T04:12:48.4866735Z 		{
2025-03-14T04:12:48.4867089Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4867533Z 			"size": 380,
2025-03-14T04:12:48.4867964Z 			"digest": "sha256:3195860f4968f586f181dbcfe1714dbac201156cad2831d1f0b2a7e7d77cd745"
2025-03-14T04:12:48.4868444Z 		},
2025-03-14T04:12:48.4868656Z 		{
2025-03-14T04:12:48.4868951Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4869227Z 			"size": 232996,
2025-03-14T04:12:48.4869522Z 			"digest": "sha256:64889f83e2762c0beecdc83ecdcda11013915f96d9a510a4d4c71d8f5c98c0d1"
2025-03-14T04:12:48.4869826Z 		},
2025-03-14T04:12:48.4869974Z 		{
2025-03-14T04:12:48.4870200Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4870471Z 			"size": 231,
2025-03-14T04:12:48.4870750Z 			"digest": "sha256:a2f45c18f0c1c2df7c02c57024d329a2fabc91c70176271b58e5d318dd3458da"
2025-03-14T04:12:48.4871047Z 		},
2025-03-14T04:12:48.4871191Z 		{
2025-03-14T04:12:48.4871405Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4871671Z 			"size": 3780000,
2025-03-14T04:12:48.4871946Z 			"digest": "sha256:16d7101d144158769623d4f9f5a88461a9f00e204f0d8eb2de4e5df6a1550c14"
2025-03-14T04:12:48.4872237Z 		},
2025-03-14T04:12:48.4872382Z 		{
2025-03-14T04:12:48.4872604Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4872865Z 			"size": 1946,
2025-03-14T04:12:48.4873149Z 			"digest": "sha256:9f39c1b701e2a249071ced1a08ec63d1cfb1b7cb8379cab2cade1bf20b3c9de2"
2025-03-14T04:12:48.4873458Z 		},
2025-03-14T04:12:48.4873598Z 		{
2025-03-14T04:12:48.4873812Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4874257Z 			"size": 105,
2025-03-14T04:12:48.4874530Z 			"digest": "sha256:2edf8a767cf2f72a7b1de53020b860979b1bdd164a8ed7ef367bd832d23d2f8b"
2025-03-14T04:12:48.4874823Z 		},
2025-03-14T04:12:48.4874966Z 		{
2025-03-14T04:12:48.4875184Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4875435Z 			"size": 802,
2025-03-14T04:12:48.4875705Z 			"digest": "sha256:6ee1d4017bc6986ed5897ef9a654bbf0ed6a7817dc066925f236df53355fcf90"
2025-03-14T04:12:48.4875998Z 		},
2025-03-14T04:12:48.4876144Z 		{
2025-03-14T04:12:48.4876359Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4876611Z 			"size": 32,
2025-03-14T04:12:48.4876876Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-14T04:12:48.4877171Z 		},
2025-03-14T04:12:48.4877311Z 		{
2025-03-14T04:12:48.4877526Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4877780Z 			"size": 104,
2025-03-14T04:12:48.4878051Z 			"digest": "sha256:aa5b1d7b7a2ced97aeb9c9ae265f99c9200cf7925c897161239a001f052f72a8"
2025-03-14T04:12:48.4878347Z 		},
2025-03-14T04:12:48.4878487Z 		{
2025-03-14T04:12:48.4878701Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4878955Z 			"size": 504,
2025-03-14T04:12:48.4879215Z 			"digest": "sha256:6100538713cbf01bbdc99f33c49d4158409d312b0d91e0da2b1b27872c295aef"
2025-03-14T04:12:48.4879499Z 		},
2025-03-14T04:12:48.4879640Z 		{
2025-03-14T04:12:48.4879857Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4880116Z 			"size": 101109876,
2025-03-14T04:12:48.4880387Z 			"digest": "sha256:f53dff608124892061f86553f7367a2bbc24bf8e87852b705ead5a9711ce0551"
2025-03-14T04:12:48.4880668Z 		},
2025-03-14T04:12:48.4880862Z 		{
2025-03-14T04:12:48.4881083Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4881345Z 			"size": 107,
2025-03-14T04:12:48.4881612Z 			"digest": "sha256:692dab4cd4df9b516d5a00637f3b139f358fac08546e782196a6b571e89db338"
2025-03-14T04:12:48.4881908Z 		},
2025-03-14T04:12:48.4882046Z 		{
2025-03-14T04:12:48.4882262Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4882523Z 			"size": 490,
2025-03-14T04:12:48.4882789Z 			"digest": "sha256:2e13cb071445f474d0c6c51ba16f9efb8107ab2b96467aee933b5f0c8724b604"
2025-03-14T04:12:48.4883080Z 		},
2025-03-14T04:12:48.4883214Z 		{
2025-03-14T04:12:48.4883432Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4883694Z 			"size": 338,
2025-03-14T04:12:48.4883949Z 			"digest": "sha256:12fc0b9855405052fa6937387f7293816b891253be753195323ce0073a151b5e"
2025-03-14T04:12:48.4884227Z 		},
2025-03-14T04:12:48.4884367Z 		{
2025-03-14T04:12:48.4884589Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4884954Z 			"size": 103,
2025-03-14T04:12:48.4885219Z 			"digest": "sha256:9c0523cdc042e8a11440408370d68e599496333bff6312078af524f2611baae4"
2025-03-14T04:12:48.4885545Z 		},
2025-03-14T04:12:48.4885743Z 		{
2025-03-14T04:12:48.4885990Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4886257Z 			"size": 1473,
2025-03-14T04:12:48.4886531Z 			"digest": "sha256:76be39b43c8ff15ab266bcad8f5744d72bc263df9d447748733c6fcf4fc1943e"
2025-03-14T04:12:48.4886827Z 		},
2025-03-14T04:12:48.4886970Z 		{
2025-03-14T04:12:48.4887182Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4887449Z 			"size": 446377208,
2025-03-14T04:12:48.4887736Z 			"digest": "sha256:a813b158a4210a11a29ed58fd3d62fa8dab8efdf6e0af85385c2885a1d1c9d55"
2025-03-14T04:12:48.4888034Z 		},
2025-03-14T04:12:48.4888177Z 		{
2025-03-14T04:12:48.4888397Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4888667Z 			"size": 164,
2025-03-14T04:12:48.4888943Z 			"digest": "sha256:bbc30b7542d1a52790dae399a0e23ec15f0ec726ef5d671b4e06e6118c912f91"
2025-03-14T04:12:48.4889305Z 		},
2025-03-14T04:12:48.4889451Z 		{
2025-03-14T04:12:48.4889673Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4889938Z 			"size": 422,
2025-03-14T04:12:48.4890210Z 			"digest": "sha256:2a30d35d5515be01fdb30110959c5f5adcd6b7e5fca723ec9c98f48f91fcb738"
2025-03-14T04:12:48.4890510Z 		},
2025-03-14T04:12:48.4890655Z 		{
2025-03-14T04:12:48.4890871Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4891133Z 			"size": 32,
2025-03-14T04:12:48.4891517Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-14T04:12:48.4891838Z 		},
2025-03-14T04:12:48.4891986Z 		{
2025-03-14T04:12:48.4892211Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4892496Z 			"size": 111,
2025-03-14T04:12:48.4892791Z 			"digest": "sha256:34c633845b891bd5eee69ead4576b3fde778f2ee4d7e65cd095848edc8946683"
2025-03-14T04:12:48.4893099Z 		},
2025-03-14T04:12:48.4893240Z 		{
2025-03-14T04:12:48.4893508Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4893770Z 			"size": 473,
2025-03-14T04:12:48.4894038Z 			"digest": "sha256:f4328165c8f338158381d4a7bc37f4596966f29a09ff5b1cfd3c9e8fbb149342"
2025-03-14T04:12:48.4894338Z 		},
2025-03-14T04:12:48.4894476Z 		{
2025-03-14T04:12:48.4894688Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4894944Z 			"size": 32,
2025-03-14T04:12:48.4895205Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-14T04:12:48.4895482Z 		},
2025-03-14T04:12:48.4895620Z 		{
2025-03-14T04:12:48.4895833Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4896086Z 			"size": 112,
2025-03-14T04:12:48.4896452Z 			"digest": "sha256:ced7420502578d83680bff46820c743cf2dc7d1f7bd6c4650fe189c940416b54"
2025-03-14T04:12:48.4896736Z 		},
2025-03-14T04:12:48.4896878Z 		{
2025-03-14T04:12:48.4897089Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4897351Z 			"size": 565,
2025-03-14T04:12:48.4897616Z 			"digest": "sha256:7543460794d1c69c1e1a7e1edba30a6239234fb5014d8b334f01f8addc38128e"
2025-03-14T04:12:48.4897902Z 		},
2025-03-14T04:12:48.4898039Z 		{
2025-03-14T04:12:48.4898252Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4898511Z 			"size": 43164080,
2025-03-14T04:12:48.4898790Z 			"digest": "sha256:81f896b48ff04356cc2ca3a784defda917a1bc7f9ccc1a3c0dbdf15b1d1cf156"
2025-03-14T04:12:48.4899103Z 		},
2025-03-14T04:12:48.4899234Z 		{
2025-03-14T04:12:48.4899444Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4899692Z 			"size": 106,
2025-03-14T04:12:48.4899951Z 			"digest": "sha256:f11b5769703741548b6910c4cf3891909c6fc2153c15ddf1596145939bc1f675"
2025-03-14T04:12:48.4900236Z 		},
2025-03-14T04:12:48.4900373Z 		{
2025-03-14T04:12:48.4900590Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4900850Z 			"size": 345,
2025-03-14T04:12:48.4901115Z 			"digest": "sha256:d09490ef7bbf73ff136691138f700147ae2c817b845e5ea9a86e1c3be639c159"
2025-03-14T04:12:48.4901407Z 		},
2025-03-14T04:12:48.4901546Z 		{
2025-03-14T04:12:48.4901763Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4902021Z 			"size": 32,
2025-03-14T04:12:48.4902286Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-14T04:12:48.4902591Z 		},
2025-03-14T04:12:48.4902724Z 		{
2025-03-14T04:12:48.4902925Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4903185Z 			"size": 106,
2025-03-14T04:12:48.4903452Z 			"digest": "sha256:595c6ea973ce7878ed25917778eaa88783efa7e652b515158353e878cedce9fa"
2025-03-14T04:12:48.4903751Z 		},
2025-03-14T04:12:48.4903889Z 		{
2025-03-14T04:12:48.4904103Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4904365Z 			"size": 425,
2025-03-14T04:12:48.4904765Z 			"digest": "sha256:b65570655a311da76613ae5622813bfe1741f803bd187d1f83d1542c73c3ceed"
2025-03-14T04:12:48.4905062Z 		},
2025-03-14T04:12:48.4905206Z 		{
2025-03-14T04:12:48.4905422Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4905683Z 			"size": 19308697,
2025-03-14T04:12:48.4905961Z 			"digest": "sha256:f3024011b7b6bb9bbd71304103e6e4c7e7cf6cc0e616150929ffcdb4660be46b"
2025-03-14T04:12:48.4906255Z 		},
2025-03-14T04:12:48.4906401Z 		{
2025-03-14T04:12:48.4906616Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4906870Z 			"size": 105,
2025-03-14T04:12:48.4907122Z 			"digest": "sha256:0b7e81f1bc5d8e9784247bb0d869e69737e76887e24b3ef6773f42405a938f63"
2025-03-14T04:12:48.4907412Z 		},
2025-03-14T04:12:48.4907562Z 		{
2025-03-14T04:12:48.4907775Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4908031Z 			"size": 643,
2025-03-14T04:12:48.4908293Z 			"digest": "sha256:c9603583aaa028021352754f45cfaedb840742c6578e1efada89ec05c07d5082"
2025-03-14T04:12:48.4908581Z 		},
2025-03-14T04:12:48.4908720Z 		{
2025-03-14T04:12:48.4908935Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4909194Z 			"size": 701,
2025-03-14T04:12:48.4909488Z 			"digest": "sha256:1e2892d1c0d6f1b67d3ba1d866912303eac78217715c2fa8631fd39c22f81996"
2025-03-14T04:12:48.4909779Z 		},
2025-03-14T04:12:48.4909923Z 		{
2025-03-14T04:12:48.4910140Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4910393Z 			"size": 143,
2025-03-14T04:12:48.4910644Z 			"digest": "sha256:68f75179d12201922607e31597da3d213875a942f8012b1c43d43c357499b646"
2025-03-14T04:12:48.4910922Z 		},
2025-03-14T04:12:48.4911055Z 		{
2025-03-14T04:12:48.4911317Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4911582Z 			"size": 136,
2025-03-14T04:12:48.4911845Z 			"digest": "sha256:2ce6ce002394ba831fcec41621aa011ca17592a66c8020b64eb1fe58505f2ed4"
2025-03-14T04:12:48.4912142Z 		},
2025-03-14T04:12:48.4912282Z 		{
2025-03-14T04:12:48.4912496Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4912759Z 			"size": 5219780266,
2025-03-14T04:12:48.4913043Z 			"digest": "sha256:cb03b7532b5e2d7fbb9a55d16acbdc1fd4f52b9cc00e62003118e2c820bbdd07"
2025-03-14T04:12:48.4913346Z 		},
2025-03-14T04:12:48.4913486Z 		{
2025-03-14T04:12:48.4913700Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4913959Z 			"size": 196,
2025-03-14T04:12:48.4914228Z 			"digest": "sha256:046ab5a4210dd05ee2a4d9ed8f1f5c86bf8ac03438dd03bb9f586578f0b40434"
2025-03-14T04:12:48.4914524Z 		},
2025-03-14T04:12:48.4914665Z 		{
2025-03-14T04:12:48.4914877Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4915139Z 			"size": 1401,
2025-03-14T04:12:48.4915416Z 			"digest": "sha256:da2b385f2f548b4fdb79d4a5f8dcaceff0a8e1c0ef59425e041cf189dfe66f74"
2025-03-14T04:12:48.4915721Z 		},
2025-03-14T04:12:48.4915863Z 		{
2025-03-14T04:12:48.4916081Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4916342Z 			"size": 701,
2025-03-14T04:12:48.4916602Z 			"digest": "sha256:1e2892d1c0d6f1b67d3ba1d866912303eac78217715c2fa8631fd39c22f81996"
2025-03-14T04:12:48.4916890Z 		},
2025-03-14T04:12:48.4917032Z 		{
2025-03-14T04:12:48.4917245Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4917501Z 			"size": 139,
2025-03-14T04:12:48.4917754Z 			"digest": "sha256:7bc635713dd5ff2321baf7a09438d605c0145a774060722495c5682045e3eed1"
2025-03-14T04:12:48.4918037Z 		},
2025-03-14T04:12:48.4918177Z 		{
2025-03-14T04:12:48.4918392Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4918648Z 			"size": 5175240887,
2025-03-14T04:12:48.4918929Z 			"digest": "sha256:cd296e79aaba46073adfef39e70b87062399ab2648d531184bdae59da7021328"
2025-03-14T04:12:48.4919223Z 		},
2025-03-14T04:12:48.4919405Z 		{
2025-03-14T04:12:48.4919628Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4919896Z 			"size": 155,
2025-03-14T04:12:48.4920168Z 			"digest": "sha256:26ea03719a99ff44da69ffcc24e8f006472a88c1160a1b48f194d012108735b3"
2025-03-14T04:12:48.4920468Z 		},
2025-03-14T04:12:48.4920612Z 		{
2025-03-14T04:12:48.4920834Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4921103Z 			"size": 1401,
2025-03-14T04:12:48.4921390Z 			"digest": "sha256:da2b385f2f548b4fdb79d4a5f8dcaceff0a8e1c0ef59425e041cf189dfe66f74"
2025-03-14T04:12:48.4921698Z 		},
2025-03-14T04:12:48.4921840Z 		{
2025-03-14T04:12:48.4922061Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4922331Z 			"size": 701,
2025-03-14T04:12:48.4922590Z 			"digest": "sha256:1e2892d1c0d6f1b67d3ba1d866912303eac78217715c2fa8631fd39c22f81996"
2025-03-14T04:12:48.4922868Z 		},
2025-03-14T04:12:48.4923009Z 		{
2025-03-14T04:12:48.4923228Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4923486Z 			"size": 139,
2025-03-14T04:12:48.4923751Z 			"digest": "sha256:f37a0227cb699ac15b09aa6ef41075ffe443681db0af5841da6553a2090fbfe6"
2025-03-14T04:12:48.4924046Z 		},
2025-03-14T04:12:48.4924186Z 		{
2025-03-14T04:12:48.4924402Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4924667Z 			"size": 32,
2025-03-14T04:12:48.4924931Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-14T04:12:48.4925224Z 		},
2025-03-14T04:12:48.4925478Z 		{
2025-03-14T04:12:48.4925701Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4925965Z 			"size": 160,
2025-03-14T04:12:48.4926287Z 			"digest": "sha256:0bd4004e84e3c2791c68438d3e95d8c82de275d45fb9a3ddf018d8d6dc8ffdc2"
2025-03-14T04:12:48.4926582Z 		},
2025-03-14T04:12:48.4926716Z 		{
2025-03-14T04:12:48.4926933Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4927200Z 			"size": 765,
2025-03-14T04:12:48.4927469Z 			"digest": "sha256:967b86e63131ae345453e13efb40580cc1c734b0eb75aa6c65aa173a45946211"
2025-03-14T04:12:48.4927786Z 		},
2025-03-14T04:12:48.4927939Z 		{
2025-03-14T04:12:48.4928170Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4928447Z 			"size": 701,
2025-03-14T04:12:48.4928730Z 			"digest": "sha256:1e2892d1c0d6f1b67d3ba1d866912303eac78217715c2fa8631fd39c22f81996"
2025-03-14T04:12:48.4929040Z 		},
2025-03-14T04:12:48.4929192Z 		{
2025-03-14T04:12:48.4929426Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4929703Z 			"size": 138,
2025-03-14T04:12:48.4929996Z 			"digest": "sha256:905d662f8eef91929aef8d9f01cfd429c3ecb16b2544892af0aeb64d98122115"
2025-03-14T04:12:48.4930312Z 		},
2025-03-14T04:12:48.4930463Z 		{
2025-03-14T04:12:48.4930687Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4930969Z 			"size": 32,
2025-03-14T04:12:48.4931256Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-14T04:12:48.4931666Z 		},
2025-03-14T04:12:48.4931825Z 		{
2025-03-14T04:12:48.4932064Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4932349Z 			"size": 159,
2025-03-14T04:12:48.4932634Z 			"digest": "sha256:39fff80b800e3735de2c6daf433e9df8cf0b4b02bcc5b2f62ba6154177fde92c"
2025-03-14T04:12:48.4932949Z 		},
2025-03-14T04:12:48.4933093Z 		{
2025-03-14T04:12:48.4933311Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4933572Z 			"size": 907,
2025-03-14T04:12:48.4933835Z 			"digest": "sha256:d681503af97af958755d5133477828ceca373627a9a0354ec6ba5f355c576422"
2025-03-14T04:12:48.4934129Z 		},
2025-03-14T04:12:48.4934272Z 		{
2025-03-14T04:12:48.4934486Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4934745Z 			"size": 701,
2025-03-14T04:12:48.4935049Z 			"digest": "sha256:1e2892d1c0d6f1b67d3ba1d866912303eac78217715c2fa8631fd39c22f81996"
2025-03-14T04:12:48.4935339Z 		},
2025-03-14T04:12:48.4935483Z 		{
2025-03-14T04:12:48.4935705Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4935966Z 			"size": 134,
2025-03-14T04:12:48.4936240Z 			"digest": "sha256:acd125116d9b9aa3745376e8edce601ca8c1cbe1f1cbd14d0d116b13ed58d436"
2025-03-14T04:12:48.4936542Z 		},
2025-03-14T04:12:48.4936687Z 		{
2025-03-14T04:12:48.4936911Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4937176Z 			"size": 32,
2025-03-14T04:12:48.4937446Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-14T04:12:48.4937741Z 		},
2025-03-14T04:12:48.4937887Z 		{
2025-03-14T04:12:48.4938104Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4938367Z 			"size": 159,
2025-03-14T04:12:48.4938631Z 			"digest": "sha256:78b00f805b53e274a95890d94e0cae256b17552d517d2a19c38760a1d8ff107b"
2025-03-14T04:12:48.4938921Z 		},
2025-03-14T04:12:48.4939064Z 		{
2025-03-14T04:12:48.4939291Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4939546Z 			"size": 1485,
2025-03-14T04:12:48.4939814Z 			"digest": "sha256:8b3d8ccc53ec9443f345526e1411cbfb5910da12fad3f39f76ee42ce0ad14e1d"
2025-03-14T04:12:48.4940103Z 		},
2025-03-14T04:12:48.4940242Z 		{
2025-03-14T04:12:48.4940456Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4940717Z 			"size": 32,
2025-03-14T04:12:48.4940986Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-14T04:12:48.4941283Z 		},
2025-03-14T04:12:48.4941428Z 		{
2025-03-14T04:12:48.4941722Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4941984Z 			"size": 135,
2025-03-14T04:12:48.4942257Z 			"digest": "sha256:07259ecbb7c987e75528fb0aa9e313d0ae77eb9a8f6e6249e4fdfd7d985a80b7"
2025-03-14T04:12:48.4942564Z 		},
2025-03-14T04:12:48.4942702Z 		{
2025-03-14T04:12:48.4942943Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4943204Z 			"size": 380,
2025-03-14T04:12:48.4943475Z 			"digest": "sha256:afabfb1e95267970e90e2058d9c09f857443f80b997adfe57208801f5b8db81b"
2025-03-14T04:12:48.4943771Z 		},
2025-03-14T04:12:48.4943917Z 		{
2025-03-14T04:12:48.4944138Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4944407Z 			"size": 32,
2025-03-14T04:12:48.4944668Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-14T04:12:48.4944957Z 		},
2025-03-14T04:12:48.4945099Z 		{
2025-03-14T04:12:48.4945315Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4945570Z 			"size": 104,
2025-03-14T04:12:48.4945836Z 			"digest": "sha256:56aa7cdd744f50ae4b9a678f5e53e7e9471f9dcf42ae8f15323d71ae01af8bd1"
2025-03-14T04:12:48.4946126Z 		},
2025-03-14T04:12:48.4946269Z 		{
2025-03-14T04:12:48.4946476Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4946734Z 			"size": 1899,
2025-03-14T04:12:48.4947004Z 			"digest": "sha256:3d413cedb292cce13e279df55899acbcf1227ea646c86a599808a03ae8b7aba9"
2025-03-14T04:12:48.4947298Z 		},
2025-03-14T04:12:48.4947439Z 		{
2025-03-14T04:12:48.4947655Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4947918Z 			"size": 196997029,
2025-03-14T04:12:48.4948197Z 			"digest": "sha256:c4d93cca7501ea10375008b5a60aef7eee5cf0d7d670fd766e7be3479726c58c"
2025-03-14T04:12:48.4948496Z 		},
2025-03-14T04:12:48.4948639Z 		{
2025-03-14T04:12:48.4948855Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4949111Z 			"size": 106,
2025-03-14T04:12:48.4949385Z 			"digest": "sha256:be17a58842f02580271fffb31b6d71f17498b862bce2cb181d9897e73ce18af5"
2025-03-14T04:12:48.4949673Z 		},
2025-03-14T04:12:48.4949814Z 		{
2025-03-14T04:12:48.4950092Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4950361Z 			"size": 163,
2025-03-14T04:12:48.4950628Z 			"digest": "sha256:af209379fbc2a1ce9c38f2383ec08f704de1b1eb23a531463224e80d937a13a3"
2025-03-14T04:12:48.4950933Z 		},
2025-03-14T04:12:48.4951081Z 		{
2025-03-14T04:12:48.4951304Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4951574Z 			"size": 7943,
2025-03-14T04:12:48.4951850Z 			"digest": "sha256:879b59207c78a7748870b6aea98a65bafa1b3f0c63ec86b59a9b142260d41250"
2025-03-14T04:12:48.4952149Z 		},
2025-03-14T04:12:48.4952294Z 		{
2025-03-14T04:12:48.4952517Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4952788Z 			"size": 8069,
2025-03-14T04:12:48.4953069Z 			"digest": "sha256:4918b9b279ff8944ea8eb1ba1ea23f23f39710864919f41470520c03f36ba04c"
2025-03-14T04:12:48.4953360Z 		},
2025-03-14T04:12:48.4953500Z 		{
2025-03-14T04:12:48.4953719Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4953990Z 			"size": 304,
2025-03-14T04:12:48.4954271Z 			"digest": "sha256:f3e4cb9dda5d3af4525d8c720afb11c9ba9a48a146bbfb268434720eb33f9b7b"
2025-03-14T04:12:48.4954570Z 		},
2025-03-14T04:12:48.4954715Z 		{
2025-03-14T04:12:48.4954942Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4955211Z 			"size": 32,
2025-03-14T04:12:48.4955483Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-14T04:12:48.4955788Z 		},
2025-03-14T04:12:48.4955929Z 		{
2025-03-14T04:12:48.4956154Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4956427Z 			"size": 106,
2025-03-14T04:12:48.4956741Z 			"digest": "sha256:da83488e1344293ac1fcf6c9ed132b6a3746e8c347dc0c5b4ebc8e24a3b73eee"
2025-03-14T04:12:48.4957044Z 		},
2025-03-14T04:12:48.4957191Z 		{
2025-03-14T04:12:48.4957411Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4957681Z 			"size": 54145653,
2025-03-14T04:12:48.4957970Z 			"digest": "sha256:78dfb83debe9f57e9eec061eb7ad61e56e2549b77b32b9ca98395ba6655ab167"
2025-03-14T04:12:48.4958274Z 		},
2025-03-14T04:12:48.4958410Z 		{
2025-03-14T04:12:48.4958629Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.4958896Z 			"size": 32,
2025-03-14T04:12:48.4959166Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-14T04:12:48.4959459Z 		}
2025-03-14T04:12:48.4959607Z 	]
2025-03-14T04:12:48.4959750Z }
2025-03-14T04:12:48.4959920Z + exit 0
2025-03-14T04:12:48.5002442Z ##[group]Run set -eux
2025-03-14T04:12:48.5002681Z set -eux
2025-03-14T04:12:48.5003232Z aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token | jq --raw-output '.SecretString' | jq -r .docker_hub_readonly_token | docker login --username pytorchbot --password-stdin
2025-03-14T04:12:48.5011919Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:48.5012355Z env:
2025-03-14T04:12:48.5012615Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:48.5012905Z ##[endgroup]
2025-03-14T04:12:48.5043866Z + jq --raw-output .SecretString
2025-03-14T04:12:48.5044544Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token
2025-03-14T04:12:48.5045118Z + jq -r .docker_hub_readonly_token
2025-03-14T04:12:48.5065207Z + docker login --username pytorchbot --password-stdin
2025-03-14T04:12:48.9810902Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-14T04:12:48.9811333Z Configure a credential helper to remove this warning. See
2025-03-14T04:12:48.9811810Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-14T04:12:48.9812103Z 
2025-03-14T04:12:48.9813774Z Login Succeeded
2025-03-14T04:12:48.9898159Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/}
2025-03-14T04:12:48.9898439Z tag=${ECR_DOCKER_IMAGE##*/}
2025-03-14T04:12:48.9898709Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}"
2025-03-14T04:12:48.9903177Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:48.9903438Z env:
2025-03-14T04:12:48.9903612Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:48.9904122Z   ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:48.9904616Z ##[endgroup]
2025-03-14T04:12:48.9925989Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks-aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:48.9965485Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main
2025-03-14T04:12:48.9965796Z with:
2025-03-14T04:12:48.9966308Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:48.9966911Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:48.9967204Z env:
2025-03-14T04:12:48.9967381Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:48.9967592Z ##[endgroup]
2025-03-14T04:12:48.9986270Z ##[group]Run set -x
2025-03-14T04:12:48.9986497Z set -x
2025-03-14T04:12:48.9986673Z set +e
2025-03-14T04:12:48.9986849Z 
2025-03-14T04:12:48.9987014Z login() {
2025-03-14T04:12:48.9987346Z   aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1"
2025-03-14T04:12:48.9987698Z }
2025-03-14T04:12:48.9987864Z 
2025-03-14T04:12:48.9988058Z retry () {
2025-03-14T04:12:48.9988261Z   $*  || (sleep 1 && $*) || (sleep 2 && $*)
2025-03-14T04:12:48.9988487Z }
2025-03-14T04:12:48.9988647Z 
2025-03-14T04:12:48.9988821Z retry login "${DOCKER_REGISTRY}"
2025-03-14T04:12:48.9989039Z 
2025-03-14T04:12:48.9989189Z set -e
2025-03-14T04:12:48.9989423Z # ignore output since only exit code is used for conditional
2025-03-14T04:12:48.9989758Z # only pull docker image if it's not available locally
2025-03-14T04:12:48.9990100Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then
2025-03-14T04:12:48.9990407Z   retry docker pull "${DOCKER_IMAGE}"
2025-03-14T04:12:48.9990621Z fi
2025-03-14T04:12:48.9994452Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:48.9994692Z env:
2025-03-14T04:12:48.9994863Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:48.9995376Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:48.9995951Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:48.9996217Z ##[endgroup]
2025-03-14T04:12:49.0020510Z + set +e
2025-03-14T04:12:49.0025628Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:49.0027605Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:49.0028054Z + aws ecr get-login-password --region us-east-1
2025-03-14T04:12:49.0028565Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:49.4131604Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-14T04:12:49.4132083Z Login Succeeded
2025-03-14T04:12:49.4135347Z Configure a credential helper to remove this warning. See
2025-03-14T04:12:49.4136015Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-14T04:12:49.4136310Z 
2025-03-14T04:12:49.4148135Z + set -e
2025-03-14T04:12:49.4148785Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:49.4299480Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:49.4305643Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:49.6569146Z aa89d6e739080d90fa18625d57297c6734465849: Pulling from pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks
2025-03-14T04:12:49.6569830Z 8f84a9f2102e: Pulling fs layer
2025-03-14T04:12:49.6570174Z f35880ae6ee6: Pulling fs layer
2025-03-14T04:12:49.6570455Z 8eb502731466: Pulling fs layer
2025-03-14T04:12:49.6571088Z d4ce6a1f04ff: Pulling fs layer
2025-03-14T04:12:49.6571357Z ca18d6003a18: Pulling fs layer
2025-03-14T04:12:49.6571745Z f6b56646e2f0: Pulling fs layer
2025-03-14T04:12:49.6571954Z 454c9d6c7abb: Pulling fs layer
2025-03-14T04:12:49.6572154Z a66b8377f49f: Pulling fs layer
2025-03-14T04:12:49.6572360Z c75867b7d634: Pulling fs layer
2025-03-14T04:12:49.6572550Z 64866485623d: Pulling fs layer
2025-03-14T04:12:49.6572757Z 04b678046b3a: Pulling fs layer
2025-03-14T04:12:49.6572944Z 1e2892d1c0d6: Pulling fs layer
2025-03-14T04:12:49.6573152Z 564c8877ceb5: Pulling fs layer
2025-03-14T04:12:49.6573339Z 8d7dafab91e2: Pulling fs layer
2025-03-14T04:12:49.6573529Z 4f4fb700ef54: Pulling fs layer
2025-03-14T04:12:49.6573712Z 3195860f4968: Pulling fs layer
2025-03-14T04:12:49.6573896Z 64889f83e276: Pulling fs layer
2025-03-14T04:12:49.6574078Z a2f45c18f0c1: Pulling fs layer
2025-03-14T04:12:49.6574259Z 16d7101d1441: Pulling fs layer
2025-03-14T04:12:49.6574441Z 9f39c1b701e2: Pulling fs layer
2025-03-14T04:12:49.6574630Z 2edf8a767cf2: Pulling fs layer
2025-03-14T04:12:49.6574809Z d4ce6a1f04ff: Waiting
2025-03-14T04:12:49.6574985Z 6ee1d4017bc6: Pulling fs layer
2025-03-14T04:12:49.6575166Z ca18d6003a18: Waiting
2025-03-14T04:12:49.6575338Z aa5b1d7b7a2c: Pulling fs layer
2025-03-14T04:12:49.6575519Z f6b56646e2f0: Waiting
2025-03-14T04:12:49.6575679Z 6100538713cb: Pulling fs layer
2025-03-14T04:12:49.6575861Z 454c9d6c7abb: Waiting
2025-03-14T04:12:49.6576037Z f53dff608124: Pulling fs layer
2025-03-14T04:12:49.6576225Z 692dab4cd4df: Pulling fs layer
2025-03-14T04:12:49.6576409Z a66b8377f49f: Waiting
2025-03-14T04:12:49.6576578Z 2e13cb071445: Pulling fs layer
2025-03-14T04:12:49.6576756Z c75867b7d634: Waiting
2025-03-14T04:12:49.6576927Z 12fc0b985540: Pulling fs layer
2025-03-14T04:12:49.6577108Z 64866485623d: Waiting
2025-03-14T04:12:49.6577279Z 9c0523cdc042: Pulling fs layer
2025-03-14T04:12:49.6577459Z 04b678046b3a: Waiting
2025-03-14T04:12:49.6577630Z 76be39b43c8f: Pulling fs layer
2025-03-14T04:12:49.6577813Z a813b158a421: Pulling fs layer
2025-03-14T04:12:49.6577996Z 1e2892d1c0d6: Waiting
2025-03-14T04:12:49.6578168Z bbc30b7542d1: Pulling fs layer
2025-03-14T04:12:49.6578342Z 564c8877ceb5: Waiting
2025-03-14T04:12:49.6578512Z 2a30d35d5515: Pulling fs layer
2025-03-14T04:12:49.6578691Z 8d7dafab91e2: Waiting
2025-03-14T04:12:49.6578861Z 34c633845b89: Pulling fs layer
2025-03-14T04:12:49.6579044Z f4328165c8f3: Pulling fs layer
2025-03-14T04:12:49.6579223Z 4f4fb700ef54: Waiting
2025-03-14T04:12:49.6579392Z ced742050257: Pulling fs layer
2025-03-14T04:12:49.6579573Z 7543460794d1: Pulling fs layer
2025-03-14T04:12:49.6579773Z 81f896b48ff0: Pulling fs layer
2025-03-14T04:12:49.6579948Z 3195860f4968: Waiting
2025-03-14T04:12:49.6580115Z f11b57697037: Pulling fs layer
2025-03-14T04:12:49.6580300Z d09490ef7bbf: Pulling fs layer
2025-03-14T04:12:49.6580483Z 595c6ea973ce: Pulling fs layer
2025-03-14T04:12:49.6580665Z b65570655a31: Pulling fs layer
2025-03-14T04:12:49.6580847Z f3024011b7b6: Pulling fs layer
2025-03-14T04:12:49.6581034Z 0b7e81f1bc5d: Pulling fs layer
2025-03-14T04:12:49.6581288Z 64889f83e276: Waiting
2025-03-14T04:12:49.6581513Z c9603583aaa0: Pulling fs layer
2025-03-14T04:12:49.6581767Z a2f45c18f0c1: Waiting
2025-03-14T04:12:49.6581998Z 68f75179d122: Pulling fs layer
2025-03-14T04:12:49.6582257Z 2ce6ce002394: Pulling fs layer
2025-03-14T04:12:49.6582504Z 16d7101d1441: Waiting
2025-03-14T04:12:49.6582741Z cb03b7532b5e: Pulling fs layer
2025-03-14T04:12:49.6583163Z 9f39c1b701e2: Waiting
2025-03-14T04:12:49.6583388Z 046ab5a4210d: Pulling fs layer
2025-03-14T04:12:49.6583577Z da2b385f2f54: Pulling fs layer
2025-03-14T04:12:49.6583761Z 7bc635713dd5: Pulling fs layer
2025-03-14T04:12:49.6583948Z cd296e79aaba: Pulling fs layer
2025-03-14T04:12:49.6584135Z 26ea03719a99: Pulling fs layer
2025-03-14T04:12:49.6584322Z 2edf8a767cf2: Waiting
2025-03-14T04:12:49.6584541Z 6ee1d4017bc6: Waiting
2025-03-14T04:12:49.6584709Z f37a0227cb69: Pulling fs layer
2025-03-14T04:12:49.6584894Z 0bd4004e84e3: Pulling fs layer
2025-03-14T04:12:49.6585160Z 967b86e63131: Pulling fs layer
2025-03-14T04:12:49.6585361Z 905d662f8eef: Pulling fs layer
2025-03-14T04:12:49.6585543Z 692dab4cd4df: Waiting
2025-03-14T04:12:49.6585704Z 39fff80b800e: Pulling fs layer
2025-03-14T04:12:49.6585883Z aa5b1d7b7a2c: Waiting
2025-03-14T04:12:49.6586056Z d681503af97a: Pulling fs layer
2025-03-14T04:12:49.6586238Z 2e13cb071445: Waiting
2025-03-14T04:12:49.6586404Z 12fc0b985540: Waiting
2025-03-14T04:12:49.6586588Z acd125116d9b: Pulling fs layer
2025-03-14T04:12:49.6586771Z 78b00f805b53: Pulling fs layer
2025-03-14T04:12:49.6586948Z 2a30d35d5515: Waiting
2025-03-14T04:12:49.6587114Z 8b3d8ccc53ec: Pulling fs layer
2025-03-14T04:12:49.6587288Z 6100538713cb: Waiting
2025-03-14T04:12:49.6587448Z 34c633845b89: Waiting
2025-03-14T04:12:49.6587605Z f37a0227cb69: Waiting
2025-03-14T04:12:49.6587768Z 07259ecbb7c9: Pulling fs layer
2025-03-14T04:12:49.6587942Z f4328165c8f3: Waiting
2025-03-14T04:12:49.6588094Z 0bd4004e84e3: Waiting
2025-03-14T04:12:49.6588261Z afabfb1e9526: Pulling fs layer
2025-03-14T04:12:49.6588440Z ced742050257: Waiting
2025-03-14T04:12:49.6588595Z 967b86e63131: Waiting
2025-03-14T04:12:49.6588748Z 7543460794d1: Waiting
2025-03-14T04:12:49.6588902Z 905d662f8eef: Waiting
2025-03-14T04:12:49.6589058Z 9c0523cdc042: Waiting
2025-03-14T04:12:49.6589220Z 56aa7cdd744f: Pulling fs layer
2025-03-14T04:12:49.6589395Z 76be39b43c8f: Waiting
2025-03-14T04:12:49.6589553Z 81f896b48ff0: Waiting
2025-03-14T04:12:49.6589712Z a813b158a421: Waiting
2025-03-14T04:12:49.6589868Z bbc30b7542d1: Waiting
2025-03-14T04:12:49.6590031Z 3d413cedb292: Pulling fs layer
2025-03-14T04:12:49.6590205Z f11b57697037: Waiting
2025-03-14T04:12:49.6590362Z d09490ef7bbf: Waiting
2025-03-14T04:12:49.6590515Z c4d93cca7501: Pulling fs layer
2025-03-14T04:12:49.6590697Z be17a58842f0: Pulling fs layer
2025-03-14T04:12:49.6590880Z af209379fbc2: Pulling fs layer
2025-03-14T04:12:49.6591058Z 879b59207c78: Pulling fs layer
2025-03-14T04:12:49.6591270Z da2b385f2f54: Waiting
2025-03-14T04:12:49.6591497Z 4918b9b279ff: Pulling fs layer
2025-03-14T04:12:49.6591767Z 7bc635713dd5: Waiting
2025-03-14T04:12:49.6592002Z 39fff80b800e: Waiting
2025-03-14T04:12:49.6592227Z cd296e79aaba: Waiting
2025-03-14T04:12:49.6592452Z d681503af97a: Waiting
2025-03-14T04:12:49.6592648Z f3e4cb9dda5d: Pulling fs layer
2025-03-14T04:12:49.6592904Z acd125116d9b: Waiting
2025-03-14T04:12:49.6593158Z cb03b7532b5e: Waiting
2025-03-14T04:12:49.6593350Z 78b00f805b53: Waiting
2025-03-14T04:12:49.6593512Z da83488e1344: Pulling fs layer
2025-03-14T04:12:49.6593697Z 78dfb83debe9: Pulling fs layer
2025-03-14T04:12:49.6593877Z 2ce6ce002394: Waiting
2025-03-14T04:12:49.6594069Z 26ea03719a99: Waiting
2025-03-14T04:12:49.6594296Z 8b3d8ccc53ec: Waiting
2025-03-14T04:12:49.6594529Z 046ab5a4210d: Waiting
2025-03-14T04:12:49.6594769Z c9603583aaa0: Waiting
2025-03-14T04:12:49.6594946Z afabfb1e9526: Waiting
2025-03-14T04:12:49.6595110Z 595c6ea973ce: Waiting
2025-03-14T04:12:49.6595274Z 56aa7cdd744f: Waiting
2025-03-14T04:12:49.6595436Z 68f75179d122: Waiting
2025-03-14T04:12:49.6595597Z 3d413cedb292: Waiting
2025-03-14T04:12:49.6595760Z c4d93cca7501: Waiting
2025-03-14T04:12:49.6595921Z f3024011b7b6: Waiting
2025-03-14T04:12:49.6596088Z b65570655a31: Waiting
2025-03-14T04:12:49.6596236Z 4918b9b279ff: Waiting
2025-03-14T04:12:49.6596393Z be17a58842f0: Waiting
2025-03-14T04:12:49.6596550Z da83488e1344: Waiting
2025-03-14T04:12:49.6596707Z f3e4cb9dda5d: Waiting
2025-03-14T04:12:49.6596955Z 78dfb83debe9: Waiting
2025-03-14T04:12:49.6597125Z f53dff608124: Waiting
2025-03-14T04:12:49.6597340Z 879b59207c78: Waiting
2025-03-14T04:12:49.6597567Z af209379fbc2: Waiting
2025-03-14T04:12:49.6597807Z 07259ecbb7c9: Waiting
2025-03-14T04:12:49.6597980Z 0b7e81f1bc5d: Waiting
2025-03-14T04:12:49.7279871Z f35880ae6ee6: Verifying Checksum
2025-03-14T04:12:49.7280186Z f35880ae6ee6: Download complete
2025-03-14T04:12:49.7964074Z d4ce6a1f04ff: Verifying Checksum
2025-03-14T04:12:49.7964420Z d4ce6a1f04ff: Download complete
2025-03-14T04:12:49.8940131Z ca18d6003a18: Verifying Checksum
2025-03-14T04:12:49.8940794Z ca18d6003a18: Download complete
2025-03-14T04:12:49.9689977Z f6b56646e2f0: Verifying Checksum
2025-03-14T04:12:49.9690299Z f6b56646e2f0: Download complete
2025-03-14T04:12:50.0319229Z 8f84a9f2102e: Verifying Checksum
2025-03-14T04:12:50.0319524Z 8f84a9f2102e: Download complete
2025-03-14T04:12:50.0998222Z 454c9d6c7abb: Verifying Checksum
2025-03-14T04:12:50.0998618Z 454c9d6c7abb: Download complete
2025-03-14T04:12:50.1340239Z a66b8377f49f: Verifying Checksum
2025-03-14T04:12:50.1346454Z a66b8377f49f: Download complete
2025-03-14T04:12:50.2026016Z 64866485623d: Download complete
2025-03-14T04:12:50.2953897Z 04b678046b3a: Verifying Checksum
2025-03-14T04:12:50.2954486Z 04b678046b3a: Download complete
2025-03-14T04:12:50.3718328Z 1e2892d1c0d6: Verifying Checksum
2025-03-14T04:12:50.3718620Z 1e2892d1c0d6: Download complete
2025-03-14T04:12:50.4781570Z 564c8877ceb5: Download complete
2025-03-14T04:12:51.0680855Z 8f84a9f2102e: Pull complete
2025-03-14T04:12:51.1030305Z f35880ae6ee6: Pull complete
2025-03-14T04:12:51.2670329Z c75867b7d634: Verifying Checksum
2025-03-14T04:12:51.2670637Z c75867b7d634: Download complete
2025-03-14T04:12:51.2772284Z 4f4fb700ef54: Verifying Checksum
2025-03-14T04:12:51.2772595Z 4f4fb700ef54: Download complete
2025-03-14T04:12:51.3495756Z 3195860f4968: Download complete
2025-03-14T04:12:51.4649710Z 64889f83e276: Download complete
2025-03-14T04:12:51.5482076Z a2f45c18f0c1: Verifying Checksum
2025-03-14T04:12:51.5482502Z a2f45c18f0c1: Download complete
2025-03-14T04:12:51.6680112Z 16d7101d1441: Verifying Checksum
2025-03-14T04:12:51.6680442Z 16d7101d1441: Download complete
2025-03-14T04:12:51.7439137Z 9f39c1b701e2: Verifying Checksum
2025-03-14T04:12:51.7443924Z 9f39c1b701e2: Download complete
2025-03-14T04:12:51.8157744Z 2edf8a767cf2: Download complete
2025-03-14T04:12:51.8974971Z 6ee1d4017bc6: Verifying Checksum
2025-03-14T04:12:51.8975488Z 6ee1d4017bc6: Download complete
2025-03-14T04:12:51.9690661Z aa5b1d7b7a2c: Verifying Checksum
2025-03-14T04:12:51.9690998Z aa5b1d7b7a2c: Download complete
2025-03-14T04:12:52.0623462Z 6100538713cb: Verifying Checksum
2025-03-14T04:12:52.0623968Z 6100538713cb: Download complete
2025-03-14T04:12:52.9178098Z 8eb502731466: Verifying Checksum
2025-03-14T04:12:52.9178433Z 8eb502731466: Download complete
2025-03-14T04:12:52.9850194Z 692dab4cd4df: Download complete
2025-03-14T04:12:53.0697305Z 2e13cb071445: Verifying Checksum
2025-03-14T04:12:53.0697682Z 2e13cb071445: Download complete
2025-03-14T04:12:53.1201547Z f53dff608124: Download complete
2025-03-14T04:12:53.1670342Z 12fc0b985540: Download complete
2025-03-14T04:12:53.1874883Z 9c0523cdc042: Download complete
2025-03-14T04:12:53.2618447Z 76be39b43c8f: Verifying Checksum
2025-03-14T04:12:53.2618832Z 76be39b43c8f: Download complete
2025-03-14T04:12:53.3226944Z bbc30b7542d1: Verifying Checksum
2025-03-14T04:12:53.3227325Z bbc30b7542d1: Download complete
2025-03-14T04:12:53.4099541Z 2a30d35d5515: Verifying Checksum
2025-03-14T04:12:53.4099866Z 2a30d35d5515: Download complete
2025-03-14T04:12:53.5703545Z f4328165c8f3: Verifying Checksum
2025-03-14T04:12:53.5703914Z f4328165c8f3: Download complete
2025-03-14T04:12:53.6532025Z ced742050257: Verifying Checksum
2025-03-14T04:12:53.6532478Z ced742050257: Download complete
2025-03-14T04:12:53.7299397Z 7543460794d1: Download complete
2025-03-14T04:12:54.2292537Z 81f896b48ff0: Verifying Checksum
2025-03-14T04:12:54.2292922Z 81f896b48ff0: Download complete
2025-03-14T04:12:54.3156897Z f11b57697037: Download complete
2025-03-14T04:12:54.4333223Z d09490ef7bbf: Verifying Checksum
2025-03-14T04:12:54.4333727Z d09490ef7bbf: Download complete
2025-03-14T04:12:54.5160173Z 595c6ea973ce: Download complete
2025-03-14T04:12:54.6126018Z b65570655a31: Download complete
2025-03-14T04:12:54.8672511Z f3024011b7b6: Verifying Checksum
2025-03-14T04:12:54.8673097Z f3024011b7b6: Download complete
2025-03-14T04:12:54.9304761Z 0b7e81f1bc5d: Verifying Checksum
2025-03-14T04:12:54.9305074Z 0b7e81f1bc5d: Download complete
2025-03-14T04:12:55.0158606Z c9603583aaa0: Verifying Checksum
2025-03-14T04:12:55.0159224Z c9603583aaa0: Download complete
2025-03-14T04:12:55.1081479Z 68f75179d122: Download complete
2025-03-14T04:12:55.1979637Z 2ce6ce002394: Verifying Checksum
2025-03-14T04:12:55.1979976Z 2ce6ce002394: Download complete
2025-03-14T04:12:57.7209092Z a813b158a421: Verifying Checksum
2025-03-14T04:12:57.7209442Z a813b158a421: Download complete
2025-03-14T04:12:57.8216360Z 046ab5a4210d: Download complete
2025-03-14T04:12:57.9164361Z da2b385f2f54: Verifying Checksum
2025-03-14T04:12:57.9164798Z da2b385f2f54: Download complete
2025-03-14T04:12:58.0066888Z 7bc635713dd5: Download complete
2025-03-14T04:13:02.3811554Z 8eb502731466: Pull complete
2025-03-14T04:13:02.3975503Z d4ce6a1f04ff: Pull complete
2025-03-14T04:13:02.4155413Z ca18d6003a18: Pull complete
2025-03-14T04:13:02.4300324Z f6b56646e2f0: Pull complete
2025-03-14T04:13:02.4688233Z 454c9d6c7abb: Pull complete
2025-03-14T04:13:02.5164300Z a66b8377f49f: Pull complete
2025-03-14T04:13:05.3069365Z c75867b7d634: Pull complete
2025-03-14T04:13:05.3403370Z 64866485623d: Pull complete
2025-03-14T04:13:05.3550926Z 04b678046b3a: Pull complete
2025-03-14T04:13:05.3687439Z 1e2892d1c0d6: Pull complete
2025-03-14T04:13:05.3839086Z 564c8877ceb5: Pull complete
2025-03-14T04:13:23.0298896Z 8d7dafab91e2: Verifying Checksum
2025-03-14T04:13:23.0299234Z 8d7dafab91e2: Download complete
2025-03-14T04:13:23.1494604Z 26ea03719a99: Verifying Checksum
2025-03-14T04:13:23.1495045Z 26ea03719a99: Download complete
2025-03-14T04:13:23.2616055Z f37a0227cb69: Verifying Checksum
2025-03-14T04:13:23.2616455Z f37a0227cb69: Download complete
2025-03-14T04:13:23.3528936Z 0bd4004e84e3: Verifying Checksum
2025-03-14T04:13:23.3529260Z 0bd4004e84e3: Download complete
2025-03-14T04:13:23.4493155Z 967b86e63131: Verifying Checksum
2025-03-14T04:13:23.4493505Z 967b86e63131: Download complete
2025-03-14T04:13:23.5821145Z 905d662f8eef: Verifying Checksum
2025-03-14T04:13:23.5821473Z 905d662f8eef: Download complete
2025-03-14T04:13:23.6627287Z 39fff80b800e: Verifying Checksum
2025-03-14T04:13:23.6627641Z 39fff80b800e: Download complete
2025-03-14T04:13:23.7351243Z d681503af97a: Verifying Checksum
2025-03-14T04:13:23.7351651Z d681503af97a: Download complete
2025-03-14T04:13:23.8276946Z acd125116d9b: Verifying Checksum
2025-03-14T04:13:23.8277252Z acd125116d9b: Download complete
2025-03-14T04:13:23.9223373Z 78b00f805b53: Verifying Checksum
2025-03-14T04:13:23.9223680Z 78b00f805b53: Download complete
2025-03-14T04:13:24.0005090Z 8b3d8ccc53ec: Verifying Checksum
2025-03-14T04:13:24.0005467Z 8b3d8ccc53ec: Download complete
2025-03-14T04:13:24.0777561Z 07259ecbb7c9: Verifying Checksum
2025-03-14T04:13:24.0777921Z 07259ecbb7c9: Download complete
2025-03-14T04:13:24.1475103Z afabfb1e9526: Verifying Checksum
2025-03-14T04:13:24.1475413Z afabfb1e9526: Download complete
2025-03-14T04:13:24.2173080Z 56aa7cdd744f: Verifying Checksum
2025-03-14T04:13:24.2173421Z 56aa7cdd744f: Download complete
2025-03-14T04:13:24.3319752Z 3d413cedb292: Verifying Checksum
2025-03-14T04:13:24.3320124Z 3d413cedb292: Download complete
2025-03-14T04:13:27.6756560Z c4d93cca7501: Verifying Checksum
2025-03-14T04:13:27.6756959Z c4d93cca7501: Download complete
2025-03-14T04:13:27.7998122Z be17a58842f0: Download complete
2025-03-14T04:13:27.8903253Z af209379fbc2: Verifying Checksum
2025-03-14T04:13:27.8903570Z af209379fbc2: Download complete
2025-03-14T04:13:27.9572510Z 879b59207c78: Download complete
2025-03-14T04:13:28.0391688Z 4918b9b279ff: Download complete
2025-03-14T04:13:28.1466468Z f3e4cb9dda5d: Verifying Checksum
2025-03-14T04:13:28.1466831Z f3e4cb9dda5d: Download complete
2025-03-14T04:13:28.2341932Z da83488e1344: Verifying Checksum
2025-03-14T04:13:28.2342304Z da83488e1344: Download complete
2025-03-14T04:13:29.3303401Z 78dfb83debe9: Download complete
2025-03-14T04:14:07.0932696Z cb03b7532b5e: Verifying Checksum
2025-03-14T04:14:07.0933106Z cb03b7532b5e: Download complete
2025-03-14T04:14:12.6706943Z cd296e79aaba: Verifying Checksum
2025-03-14T04:14:12.6710961Z cd296e79aaba: Download complete
2025-03-14T04:14:22.1898699Z 8d7dafab91e2: Pull complete
2025-03-14T04:14:22.5388002Z 4f4fb700ef54: Pull complete
2025-03-14T04:14:22.8079528Z 3195860f4968: Pull complete
2025-03-14T04:14:23.2199954Z 64889f83e276: Pull complete
2025-03-14T04:14:23.5127530Z a2f45c18f0c1: Pull complete
2025-03-14T04:14:23.9498791Z 16d7101d1441: Pull complete
2025-03-14T04:14:24.0940050Z 9f39c1b701e2: Pull complete
2025-03-14T04:14:24.4646288Z 2edf8a767cf2: Pull complete
2025-03-14T04:14:24.7666797Z 6ee1d4017bc6: Pull complete
2025-03-14T04:14:25.1486313Z aa5b1d7b7a2c: Pull complete
2025-03-14T04:14:25.4551970Z 6100538713cb: Pull complete
2025-03-14T04:14:28.8150161Z f53dff608124: Pull complete
2025-03-14T04:14:29.2905631Z 692dab4cd4df: Pull complete
2025-03-14T04:14:29.7780514Z 2e13cb071445: Pull complete
2025-03-14T04:14:30.2322535Z 12fc0b985540: Pull complete
2025-03-14T04:14:30.5294155Z 9c0523cdc042: Pull complete
2025-03-14T04:14:30.7925456Z 76be39b43c8f: Pull complete
2025-03-14T04:14:41.3196352Z a813b158a421: Pull complete
2025-03-14T04:14:41.8279577Z bbc30b7542d1: Pull complete
2025-03-14T04:14:42.2671069Z 2a30d35d5515: Pull complete
2025-03-14T04:14:42.9958765Z 34c633845b89: Pull complete
2025-03-14T04:14:43.3390342Z f4328165c8f3: Pull complete
2025-03-14T04:14:43.8293684Z ced742050257: Pull complete
2025-03-14T04:14:44.1020386Z 7543460794d1: Pull complete
2025-03-14T04:14:46.8431115Z 81f896b48ff0: Pull complete
2025-03-14T04:14:47.1519217Z f11b57697037: Pull complete
2025-03-14T04:14:47.5376692Z d09490ef7bbf: Pull complete
2025-03-14T04:14:48.3052981Z 595c6ea973ce: Pull complete
2025-03-14T04:14:48.7446061Z b65570655a31: Pull complete
2025-03-14T04:14:49.4893860Z f3024011b7b6: Pull complete
2025-03-14T04:14:49.9560770Z 0b7e81f1bc5d: Pull complete
2025-03-14T04:14:50.3864728Z c9603583aaa0: Pull complete
2025-03-14T04:14:51.2899066Z 68f75179d122: Pull complete
2025-03-14T04:14:51.8264977Z 2ce6ce002394: Pull complete
2025-03-14T04:15:39.2089663Z cb03b7532b5e: Pull complete
2025-03-14T04:15:39.7367870Z 046ab5a4210d: Pull complete
2025-03-14T04:15:40.1718411Z da2b385f2f54: Pull complete
2025-03-14T04:15:40.8818078Z 7bc635713dd5: Pull complete
2025-03-14T04:17:26.5481625Z cd296e79aaba: Pull complete
2025-03-14T04:17:27.0819107Z 26ea03719a99: Pull complete
2025-03-14T04:17:28.6285608Z f37a0227cb69: Pull complete
2025-03-14T04:17:29.5052724Z 0bd4004e84e3: Pull complete
2025-03-14T04:17:29.9981870Z 967b86e63131: Pull complete
2025-03-14T04:17:30.9048388Z 905d662f8eef: Pull complete
2025-03-14T04:17:31.8249433Z 39fff80b800e: Pull complete
2025-03-14T04:17:32.2617362Z d681503af97a: Pull complete
2025-03-14T04:17:33.3237158Z acd125116d9b: Pull complete
2025-03-14T04:17:34.2035238Z 78b00f805b53: Pull complete
2025-03-14T04:17:34.7802059Z 8b3d8ccc53ec: Pull complete
2025-03-14T04:17:35.7592669Z 07259ecbb7c9: Pull complete
2025-03-14T04:17:36.2469983Z afabfb1e9526: Pull complete
2025-03-14T04:17:37.2080212Z 56aa7cdd744f: Pull complete
2025-03-14T04:17:37.5516651Z 3d413cedb292: Pull complete
2025-03-14T04:17:44.4674747Z c4d93cca7501: Pull complete
2025-03-14T04:17:44.9128250Z be17a58842f0: Pull complete
2025-03-14T04:17:45.3551812Z af209379fbc2: Pull complete
2025-03-14T04:17:45.8190238Z 879b59207c78: Pull complete
2025-03-14T04:17:46.3046422Z 4918b9b279ff: Pull complete
2025-03-14T04:17:46.8222133Z f3e4cb9dda5d: Pull complete
2025-03-14T04:17:47.7035554Z da83488e1344: Pull complete
2025-03-14T04:17:50.0442285Z 78dfb83debe9: Pull complete
2025-03-14T04:17:50.6355461Z Digest: sha256:2f16eb7d476b5dc359eb789543b0cfc9aa5c04fe105d51acd219f91259bad5ab
2025-03-14T04:17:50.7165010Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:17:50.7425754Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:17:50.7468181Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:17:50.7468759Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:17:50.7474723Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:17:50.7474972Z env:
2025-03-14T04:17:50.7475137Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:50.7475323Z ##[endgroup]
2025-03-14T04:17:50.7547332Z Prepare all required actions
2025-03-14T04:17:50.7786224Z ##[group]Run ./.github/actions/get-workflow-job-id
2025-03-14T04:17:50.7786463Z with:
2025-03-14T04:17:50.7786809Z   github-token: ***
2025-03-14T04:17:50.7786971Z env:
2025-03-14T04:17:50.7787125Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:50.7787307Z ##[endgroup]
2025-03-14T04:17:50.7945056Z ##[group]Run set -eux
2025-03-14T04:17:50.7945282Z set -eux
2025-03-14T04:17:50.7945570Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}"
2025-03-14T04:17:50.7949856Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:17:50.7950107Z env:
2025-03-14T04:17:50.7950265Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:50.7950612Z   GITHUB_TOKEN: ***
2025-03-14T04:17:50.7950781Z ##[endgroup]
2025-03-14T04:17:50.7974800Z + python3 .github/scripts/get_workflow_job_id.py 13849515380 i-00f46405241e0bbc9
2025-03-14T04:17:51.3884399Z setting job-id=38754842362
2025-03-14T04:17:51.3888403Z setting job-name=linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:17:51.4118379Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7
2025-03-14T04:17:51.4118867Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7
2025-03-14T04:17:51.4119250Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 &
2025-03-14T04:17:51.4119563Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:17:51.4123902Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:17:51.4124157Z env:
2025-03-14T04:17:51.4124330Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:51.4124523Z   JOB_ID: 38754842362
2025-03-14T04:17:51.4124876Z   JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:17:51.4125256Z   WORKFLOW_NAME: inductor
2025-03-14T04:17:51.4125448Z   WORKFLOW_RUN_ID: 13849515380
2025-03-14T04:17:51.4125657Z ##[endgroup]
2025-03-14T04:17:51.5852043Z Defaulting to user installation because normal site-packages is not writeable
2025-03-14T04:17:51.5990520Z Requirement already satisfied: psutil==5.9.1 in /home/ec2-user/.local/lib/python3.9/site-packages (5.9.1)
2025-03-14T04:17:51.5992321Z Requirement already satisfied: nvidia-ml-py==11.525.84 in /home/ec2-user/.local/lib/python3.9/site-packages (11.525.84)
2025-03-14T04:17:51.5993079Z Requirement already satisfied: dataclasses_json==0.6.7 in /home/ec2-user/.local/lib/python3.9/site-packages (0.6.7)
2025-03-14T04:17:51.6070416Z Requirement already satisfied: typing-inspect<1,>=0.4.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from dataclasses_json==0.6.7) (0.9.0)
2025-03-14T04:17:51.6072411Z Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from dataclasses_json==0.6.7) (3.26.1)
2025-03-14T04:17:51.6130897Z Requirement already satisfied: packaging>=17.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses_json==0.6.7) (24.2)
2025-03-14T04:17:51.6151524Z Requirement already satisfied: mypy-extensions>=0.3.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from typing-inspect<1,>=0.4.0->dataclasses_json==0.6.7) (1.0.0)
2025-03-14T04:17:51.6152473Z Requirement already satisfied: typing-extensions>=3.7.4 in /home/ec2-user/.local/lib/python3.9/site-packages (from typing-inspect<1,>=0.4.0->dataclasses_json==0.6.7) (4.12.2)
2025-03-14T04:17:51.7667145Z Prepare all required actions
2025-03-14T04:17:51.7667473Z Getting action download info
2025-03-14T04:17:51.8657171Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6)
2025-03-14T04:17:52.8827711Z Download action repository 'actions/download-artifact@v4' (SHA:cc203385981b70ca67e1cc392babf9cc229d5806)
2025-03-14T04:17:54.9442899Z ##[group]Run ./.github/actions/download-build-artifacts
2025-03-14T04:17:54.9443199Z with:
2025-03-14T04:17:54.9443394Z   name: linux-jammy-py3.9-gcc11-build
2025-03-14T04:17:54.9443634Z   s3-bucket: gha-artifacts
2025-03-14T04:17:54.9443836Z env:
2025-03-14T04:17:54.9444009Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:54.9444201Z ##[endgroup]
2025-03-14T04:17:54.9474099Z ##[group]Run seemethere/download-artifact-s3@v4
2025-03-14T04:17:54.9474341Z with:
2025-03-14T04:17:54.9474522Z   name: linux-jammy-py3.9-gcc11-build
2025-03-14T04:17:54.9474741Z   s3-bucket: gha-artifacts
2025-03-14T04:17:54.9505565Z   region: us-east-1
2025-03-14T04:17:54.9505775Z env:
2025-03-14T04:17:54.9505943Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:54.9506138Z ##[endgroup]
2025-03-14T04:17:55.3393135Z (node:225448) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023.
2025-03-14T04:17:55.3393618Z 
2025-03-14T04:17:55.3394140Z Please migrate your code to use AWS SDK for JavaScript (v3).
2025-03-14T04:17:55.3394504Z For more information, check the migration guide at https://a.co/7PzMCcy
2025-03-14T04:17:55.3394914Z (Use `node --trace-warnings ...` to show where the warning was created)
2025-03-14T04:17:55.4261836Z Found 1 objects with prefix pytorch/pytorch/13849515380/linux-jammy-py3.9-gcc11-build/
2025-03-14T04:17:55.4263822Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2025-03-14T04:17:59.4042464Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2025-03-14T04:17:59.4045372Z Artifact download has finished successfully
2025-03-14T04:17:59.4219438Z ##[group]Run unzip -o artifacts.zip
2025-03-14T04:17:59.4219701Z unzip -o artifacts.zip
2025-03-14T04:17:59.4223954Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:17:59.4224214Z env:
2025-03-14T04:17:59.4224380Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:59.4224563Z ##[endgroup]
2025-03-14T04:17:59.4263460Z Archive:  artifacts.zip
2025-03-14T04:17:59.4264119Z    creating: dist/
2025-03-14T04:18:00.3164156Z   inflating: dist/torch-2.8.0a0+gitaed0b7a-cp39-cp39-linux_x86_64.whl  
2025-03-14T04:18:00.3273922Z   inflating: dist/.ninja_log         
2025-03-14T04:18:00.3274245Z    creating: build/custom_test_artifacts/
2025-03-14T04:18:00.3274536Z    creating: build/custom_test_artifacts/custom-op-build/
2025-03-14T04:18:00.3274872Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/
2025-03-14T04:18:00.3275249Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/pkgRedirects/
2025-03-14T04:18:00.3279595Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeConfigureLog.yaml  
2025-03-14T04:18:00.3280176Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.31.2/
2025-03-14T04:18:00.3280675Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.31.2/CMakeSystem.cmake  
2025-03-14T04:18:00.3281190Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.31.2/CompilerIdC/
2025-03-14T04:18:00.3281910Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.31.2/CompilerIdC/tmp/
2025-03-14T04:18:00.3282376Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.31.2/CompilerIdC/CMakeCCompilerId.c  
2025-03-14T04:18:00.3283682Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.31.2/CompilerIdC/a.out  
2025-03-14T04:18:00.3292137Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.31.2/CMakeCCompiler.cmake  
2025-03-14T04:18:00.3293048Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.31.2/CompilerIdCXX/
2025-03-14T04:18:00.3293480Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.31.2/CompilerIdCXX/tmp/
2025-03-14T04:18:00.3302448Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.31.2/CompilerIdCXX/CMakeCXXCompilerId.cpp  
2025-03-14T04:18:00.3312495Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.31.2/CompilerIdCXX/a.out  
2025-03-14T04:18:00.3313029Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.31.2/CMakeCXXCompiler.cmake  
2025-03-14T04:18:00.3313539Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.31.2/CMakeDetermineCompilerABI_C.bin  
2025-03-14T04:18:00.3315169Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.31.2/CMakeDetermineCompilerABI_CXX.bin  
2025-03-14T04:18:00.3315639Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeScratch/
2025-03-14T04:18:00.3324732Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/cmake.check_cache  
2025-03-14T04:18:00.3325192Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/
2025-03-14T04:18:00.3325639Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/compiler_depend.ts  
2025-03-14T04:18:00.3326129Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/compiler_depend.make  
2025-03-14T04:18:00.3326596Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/depend.make  
2025-03-14T04:18:00.3327045Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/link.txt  
2025-03-14T04:18:00.3327496Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/cmake_clean.cmake  
2025-03-14T04:18:00.3332990Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/build.make  
2025-03-14T04:18:00.3333567Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/DependInfo.cmake  
2025-03-14T04:18:00.3340639Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/flags.make  
2025-03-14T04:18:00.3354992Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/progress.make  
2025-03-14T04:18:00.3381436Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/op.cpp.o.d  
2025-03-14T04:18:00.3543143Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/op.cpp.o  
2025-03-14T04:18:00.3543659Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/
2025-03-14T04:18:00.3544127Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/compiler_depend.ts  
2025-03-14T04:18:00.3544644Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/compiler_depend.make  
2025-03-14T04:18:00.3545143Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/depend.make  
2025-03-14T04:18:00.3545604Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/link.txt  
2025-03-14T04:18:00.3546078Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/cmake_clean.cmake  
2025-03-14T04:18:00.3546559Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/build.make  
2025-03-14T04:18:00.3547284Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/DependInfo.cmake  
2025-03-14T04:18:00.3554821Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/flags.make  
2025-03-14T04:18:00.3555350Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/progress.make  
2025-03-14T04:18:00.3572150Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/test_custom_ops.cpp.o.d  
2025-03-14T04:18:00.3643092Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/test_custom_ops.cpp.o  
2025-03-14T04:18:00.3643751Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeDirectoryInformation.cmake  
2025-03-14T04:18:00.3644350Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/TargetDirectories.txt  
2025-03-14T04:18:00.3644879Z  extracting: build/custom_test_artifacts/custom-op-build/CMakeFiles/progress.marks  
2025-03-14T04:18:00.3645484Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/Makefile2  
2025-03-14T04:18:00.3645889Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/Makefile.cmake  
2025-03-14T04:18:00.3646947Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeCache.txt  
2025-03-14T04:18:00.3649093Z   inflating: build/custom_test_artifacts/custom-op-build/Makefile  
2025-03-14T04:18:00.3649552Z   inflating: build/custom_test_artifacts/custom-op-build/cmake_install.cmake  
2025-03-14T04:18:00.3797081Z   inflating: build/custom_test_artifacts/custom-op-build/libcustom_ops.so  
2025-03-14T04:18:00.3866079Z   inflating: build/custom_test_artifacts/custom-op-build/test_custom_ops  
2025-03-14T04:18:00.3873429Z    creating: build/custom_test_artifacts/jit-hook-build/
2025-03-14T04:18:00.3934349Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/
2025-03-14T04:18:00.3934790Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/pkgRedirects/
2025-03-14T04:18:00.3935257Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/CMakeConfigureLog.yaml  
2025-03-14T04:18:00.3935680Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.31.2/
2025-03-14T04:18:00.3936092Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.31.2/CMakeSystem.cmake  
2025-03-14T04:18:00.3936532Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.31.2/CompilerIdC/
2025-03-14T04:18:00.3936952Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.31.2/CompilerIdC/tmp/
2025-03-14T04:18:00.3937414Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.31.2/CompilerIdC/CMakeCCompilerId.c  
2025-03-14T04:18:00.3937881Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.31.2/CompilerIdC/a.out  
2025-03-14T04:18:00.3938317Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.31.2/CMakeCCompiler.cmake  
2025-03-14T04:18:00.3938749Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.31.2/CompilerIdCXX/
2025-03-14T04:18:00.3939191Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.31.2/CompilerIdCXX/tmp/
2025-03-14T04:18:00.3939675Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.31.2/CompilerIdCXX/CMakeCXXCompilerId.cpp  
2025-03-14T04:18:00.3940181Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.31.2/CompilerIdCXX/a.out  
2025-03-14T04:18:00.3940622Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.31.2/CMakeCXXCompiler.cmake  
2025-03-14T04:18:00.3941090Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.31.2/CMakeDetermineCompilerABI_C.bin  
2025-03-14T04:18:00.3941581Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.31.2/CMakeDetermineCompilerABI_CXX.bin  
2025-03-14T04:18:00.3942015Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/CMakeScratch/
2025-03-14T04:18:00.3942387Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/cmake.check_cache  
2025-03-14T04:18:00.3943039Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/
2025-03-14T04:18:00.3943479Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/compiler_depend.ts  
2025-03-14T04:18:00.3943975Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/compiler_depend.make  
2025-03-14T04:18:00.3944552Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/depend.make  
2025-03-14T04:18:00.3944999Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/link.txt  
2025-03-14T04:18:00.3945453Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/cmake_clean.cmake  
2025-03-14T04:18:00.3945913Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/build.make  
2025-03-14T04:18:00.3946371Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/DependInfo.cmake  
2025-03-14T04:18:00.3946833Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/flags.make  
2025-03-14T04:18:00.3947279Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/progress.make  
2025-03-14T04:18:00.3947762Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/test_jit_hooks.cpp.o.d  
2025-03-14T04:18:00.3978828Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/test_jit_hooks.cpp.o  
2025-03-14T04:18:00.3979408Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/CMakeDirectoryInformation.cmake  
2025-03-14T04:18:00.3979886Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/TargetDirectories.txt  
2025-03-14T04:18:00.3980311Z  extracting: build/custom_test_artifacts/jit-hook-build/CMakeFiles/progress.marks  
2025-03-14T04:18:00.3980699Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/Makefile2  
2025-03-14T04:18:00.3981089Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/Makefile.cmake  
2025-03-14T04:18:00.3986993Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeCache.txt  
2025-03-14T04:18:00.4012763Z   inflating: build/custom_test_artifacts/jit-hook-build/Makefile  
2025-03-14T04:18:00.4013170Z   inflating: build/custom_test_artifacts/jit-hook-build/cmake_install.cmake  
2025-03-14T04:18:00.4045274Z   inflating: build/custom_test_artifacts/jit-hook-build/test_jit_hooks  
2025-03-14T04:18:00.4045808Z    creating: build/custom_test_artifacts/custom-backend-build/
2025-03-14T04:18:00.4046209Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/
2025-03-14T04:18:00.4046659Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/pkgRedirects/
2025-03-14T04:18:00.4047166Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/CMakeConfigureLog.yaml  
2025-03-14T04:18:00.4047674Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.31.2/
2025-03-14T04:18:00.4048171Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.31.2/CMakeSystem.cmake  
2025-03-14T04:18:00.4048694Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.31.2/CompilerIdC/
2025-03-14T04:18:00.4049208Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.31.2/CompilerIdC/tmp/
2025-03-14T04:18:00.4049779Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.31.2/CompilerIdC/CMakeCCompilerId.c  
2025-03-14T04:18:00.4050362Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.31.2/CompilerIdC/a.out  
2025-03-14T04:18:00.4050904Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.31.2/CMakeCCompiler.cmake  
2025-03-14T04:18:00.4051418Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.31.2/CompilerIdCXX/
2025-03-14T04:18:00.4052313Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.31.2/CompilerIdCXX/tmp/
2025-03-14T04:18:00.4053209Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.31.2/CompilerIdCXX/CMakeCXXCompilerId.cpp  
2025-03-14T04:18:00.4054161Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.31.2/CompilerIdCXX/a.out  
2025-03-14T04:18:00.4054969Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.31.2/CMakeCXXCompiler.cmake  
2025-03-14T04:18:00.4056313Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.31.2/CMakeDetermineCompilerABI_C.bin  
2025-03-14T04:18:00.4057445Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.31.2/CMakeDetermineCompilerABI_CXX.bin  
2025-03-14T04:18:00.4057953Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/CMakeScratch/
2025-03-14T04:18:00.4058386Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/cmake.check_cache  
2025-03-14T04:18:00.4058838Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/
2025-03-14T04:18:00.4059338Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/compiler_depend.ts  
2025-03-14T04:18:00.4059896Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/compiler_depend.make  
2025-03-14T04:18:00.4060437Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/depend.make  
2025-03-14T04:18:00.4061146Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/link.txt  
2025-03-14T04:18:00.4061664Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/cmake_clean.cmake  
2025-03-14T04:18:00.4062187Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/build.make  
2025-03-14T04:18:00.4062707Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/DependInfo.cmake  
2025-03-14T04:18:00.4063234Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/flags.make  
2025-03-14T04:18:00.4063742Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/progress.make  
2025-03-14T04:18:00.4065345Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/custom_backend.cpp.o.d  
2025-03-14T04:18:00.4171169Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/custom_backend.cpp.o  
2025-03-14T04:18:00.4172931Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/
2025-03-14T04:18:00.4173512Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/compiler_depend.ts  
2025-03-14T04:18:00.4174084Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/compiler_depend.make  
2025-03-14T04:18:00.4174648Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/depend.make  
2025-03-14T04:18:00.4175153Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/link.txt  
2025-03-14T04:18:00.4175669Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/cmake_clean.cmake  
2025-03-14T04:18:00.4176190Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/build.make  
2025-03-14T04:18:00.4176710Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/DependInfo.cmake  
2025-03-14T04:18:00.4177229Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/flags.make  
2025-03-14T04:18:00.4177744Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/progress.make  
2025-03-14T04:18:00.4190223Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/test_custom_backend.cpp.o.d  
2025-03-14T04:18:00.4236897Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/test_custom_backend.cpp.o  
2025-03-14T04:18:00.4237527Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/CMakeDirectoryInformation.cmake  
2025-03-14T04:18:00.4238286Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/TargetDirectories.txt  
2025-03-14T04:18:00.4238764Z  extracting: build/custom_test_artifacts/custom-backend-build/CMakeFiles/progress.marks  
2025-03-14T04:18:00.4239189Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/Makefile2  
2025-03-14T04:18:00.4239605Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/Makefile.cmake  
2025-03-14T04:18:00.4240016Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeCache.txt  
2025-03-14T04:18:00.4240388Z   inflating: build/custom_test_artifacts/custom-backend-build/Makefile  
2025-03-14T04:18:00.4240763Z   inflating: build/custom_test_artifacts/custom-backend-build/cmake_install.cmake  
2025-03-14T04:18:00.4330278Z   inflating: build/custom_test_artifacts/custom-backend-build/libcustom_backend.so  
2025-03-14T04:18:00.4369182Z   inflating: build/custom_test_artifacts/custom-backend-build/test_custom_backend  
2025-03-14T04:18:00.4369597Z    creating: build/lib/
2025-03-14T04:18:00.4442559Z   inflating: build/lib/libprotobuf-lite.a  
2025-03-14T04:18:00.4831073Z   inflating: build/lib/libprotobuf.a  
2025-03-14T04:18:00.5309164Z   inflating: build/lib/libprotoc.a   
2025-03-14T04:18:00.5311005Z   inflating: build/lib/libpthreadpool.a  
2025-03-14T04:18:00.5311374Z   inflating: build/lib/libcpuinfo.a  
2025-03-14T04:18:00.5312524Z   inflating: build/lib/libcpuinfo_internals.a  
2025-03-14T04:18:00.5312800Z   inflating: build/lib/libclog.a     
2025-03-14T04:18:00.5313085Z   inflating: build/lib/libnnpack_reference_layers.a  
2025-03-14T04:18:00.5313353Z   inflating: build/lib/libpytorch_qnnpack.a  
2025-03-14T04:18:00.5473589Z   inflating: build/lib/libmicrokernels-prod.a  
2025-03-14T04:18:00.5488155Z   inflating: build/lib/libnnpack.a   
2025-03-14T04:18:00.6258081Z   inflating: build/lib/libmicrokernels-all.a  
2025-03-14T04:18:00.6318594Z   inflating: build/lib/libgtest.a    
2025-03-14T04:18:00.6331224Z   inflating: build/lib/libgmock.a    
2025-03-14T04:18:00.6331802Z   inflating: build/lib/libgtest_main.a  
2025-03-14T04:18:00.6332194Z   inflating: build/lib/libgmock_main.a  
2025-03-14T04:18:00.6411371Z   inflating: build/lib/libXNNPACK.a  
2025-03-14T04:18:00.6478390Z   inflating: build/lib/libbenchmark.a  
2025-03-14T04:18:00.6483325Z   inflating: build/lib/libbenchmark_main.a  
2025-03-14T04:18:00.6488240Z   inflating: build/lib/libittnotify.a  
2025-03-14T04:18:00.6542371Z   inflating: build/lib/libasmjit.a   
2025-03-14T04:18:00.7434732Z   inflating: build/lib/libfbgemm.a   
2025-03-14T04:18:00.7458286Z   inflating: build/lib/libtensorpipe_uv.a  
2025-03-14T04:18:00.7938821Z   inflating: build/lib/libtensorpipe.a  
2025-03-14T04:18:00.8036512Z   inflating: build/lib/libgloo.a     
2025-03-14T04:18:00.8071000Z   inflating: build/lib/libonnx_proto.a  
2025-03-14T04:18:00.8705907Z   inflating: build/lib/libonnx.a     
2025-03-14T04:18:01.7581499Z   inflating: build/lib/libdnnl.a     
2025-03-14T04:18:01.7599146Z   inflating: build/lib/libfmt.a      
2025-03-14T04:18:01.7833046Z   inflating: build/lib/libkineto.a   
2025-03-14T04:18:01.7932038Z   inflating: build/lib/libc10.so     
2025-03-14T04:18:01.7932377Z   inflating: build/lib/libtorch_global_deps.so  
2025-03-14T04:18:03.9425085Z   inflating: build/lib/libtorch_cpu.so  
2025-03-14T04:18:03.9430501Z   inflating: build/lib/libunbox_lib.a  
2025-03-14T04:18:03.9431015Z   inflating: build/lib/libtorch.so   
2025-03-14T04:18:03.9491804Z   inflating: build/lib/libtorchbind_test.so  
2025-03-14T04:18:03.9510792Z   inflating: build/lib/libjitbackend_test.so  
2025-03-14T04:18:03.9533028Z   inflating: build/lib/libbackend_with_compiler.so  
2025-03-14T04:18:03.9555464Z   inflating: build/lib/libaoti_custom_ops.so  
2025-03-14T04:18:03.9557444Z   inflating: build/lib/libshm.so     
2025-03-14T04:18:04.1254787Z   inflating: build/lib/libtorch_python.so  
2025-03-14T04:18:04.1285128Z   inflating: build/lib/libnnapi_backend.so  
2025-03-14T04:18:04.1285598Z    creating: build/bin/
2025-03-14T04:18:04.1286275Z    creating: build/bin/CMakeFiles/
2025-03-14T04:18:04.1286536Z   inflating: build/bin/cmake_install.cmake  
2025-03-14T04:18:04.1286782Z   inflating: build/bin/CTestTestfile.cmake  
2025-03-14T04:18:04.1696980Z   inflating: build/bin/protoc-3.13.0.0  
2025-03-14T04:18:04.2112268Z   inflating: build/bin/protoc        
2025-03-14T04:18:04.2161421Z   inflating: build/bin/c10_CompileTimeFunctionPointer_test  
2025-03-14T04:18:04.2212119Z   inflating: build/bin/c10_DeviceGuard_test  
2025-03-14T04:18:04.2264548Z   inflating: build/bin/c10_Device_test  
2025-03-14T04:18:04.2318353Z   inflating: build/bin/c10_DispatchKeySet_test  
2025-03-14T04:18:04.2369793Z   inflating: build/bin/c10_StreamGuard_test  
2025-03-14T04:18:04.2424071Z   inflating: build/bin/c10_Scalar_test  
2025-03-14T04:18:04.2473311Z   inflating: build/bin/c10_SymInt_test  
2025-03-14T04:18:04.2526849Z   inflating: build/bin/c10_InlineStreamGuard_test  
2025-03-14T04:18:04.2581878Z   inflating: build/bin/c10_InlineDeviceGuard_test  
2025-03-14T04:18:04.2638607Z   inflating: build/bin/c10_SizesAndStrides_test  
2025-03-14T04:18:04.2705982Z   inflating: build/bin/c10_cow_test  
2025-03-14T04:18:04.2753808Z   inflating: build/bin/c10_ConstexprCrc_test  
2025-03-14T04:18:04.2803677Z   inflating: build/bin/c10_ArrayRef_test  
2025-03-14T04:18:04.2857929Z   inflating: build/bin/c10_Bitset_test  
2025-03-14T04:18:04.2904853Z   inflating: build/bin/c10_DeadlockDetection_test  
2025-03-14T04:18:04.2957609Z   inflating: build/bin/c10_Half_test  
2025-03-14T04:18:04.3012281Z   inflating: build/bin/c10_LeftRight_test  
2025-03-14T04:18:04.3066491Z   inflating: build/bin/c10_NetworkFlow_test  
2025-03-14T04:18:04.3122982Z   inflating: build/bin/c10_Metaprogramming_test  
2025-03-14T04:18:04.3170876Z   inflating: build/bin/c10_Synchronized_test  
2025-03-14T04:18:04.3224648Z   inflating: build/bin/c10_TypeIndex_test  
2025-03-14T04:18:04.3278562Z   inflating: build/bin/c10_ThreadLocal_test  
2025-03-14T04:18:04.3327378Z   inflating: build/bin/c10_TypeList_test  
2025-03-14T04:18:04.3379053Z   inflating: build/bin/c10_TypeTraits_test  
2025-03-14T04:18:04.3427162Z   inflating: build/bin/c10_accumulate_test  
2025-03-14T04:18:04.3478162Z   inflating: build/bin/c10_bit_cast_test  
2025-03-14T04:18:04.3535111Z   inflating: build/bin/c10_complex_math_test  
2025-03-14T04:18:04.3590924Z   inflating: build/bin/c10_bfloat16_test  
2025-03-14T04:18:04.3646076Z   inflating: build/bin/c10_complex_test  
2025-03-14T04:18:04.3695676Z   inflating: build/bin/c10_error_test  
2025-03-14T04:18:04.3747175Z   inflating: build/bin/c10_exception_test  
2025-03-14T04:18:04.3808563Z   inflating: build/bin/c10_flags_test  
2025-03-14T04:18:04.3951653Z   inflating: build/bin/c10_intrusive_ptr_test  
2025-03-14T04:18:04.3998346Z   inflating: build/bin/c10_generic_math_test  
2025-03-14T04:18:04.4049450Z   inflating: build/bin/c10_irange_test  
2025-03-14T04:18:04.4103078Z   inflating: build/bin/c10_lazy_test  
2025-03-14T04:18:04.4162581Z   inflating: build/bin/c10_logging_test  
2025-03-14T04:18:04.4213867Z   inflating: build/bin/c10_registry_test  
2025-03-14T04:18:04.4275876Z   inflating: build/bin/c10_ordered_preserving_dict_test  
2025-03-14T04:18:04.4348350Z   inflating: build/bin/c10_optional_test  
2025-03-14T04:18:04.4492802Z   inflating: build/bin/c10_small_vector_test  
2025-03-14T04:18:04.4544569Z   inflating: build/bin/c10_ssize_test  
2025-03-14T04:18:04.4598132Z   inflating: build/bin/c10_string_util_test  
2025-03-14T04:18:04.4648272Z   inflating: build/bin/c10_tempfile_test  
2025-03-14T04:18:04.4691091Z   inflating: build/bin/c10_intrusive_ptr_benchmark  
2025-03-14T04:18:04.4741478Z   inflating: build/bin/c10_string_view_test  
2025-03-14T04:18:04.4797590Z   inflating: build/bin/c10_typeid_test  
2025-03-14T04:18:04.5162635Z   inflating: build/bin/vec_test_all_types_DEFAULT  
2025-03-14T04:18:04.5539181Z   inflating: build/bin/vec_test_all_types_AVX512  
2025-03-14T04:18:04.5930193Z   inflating: build/bin/vec_test_all_types_AVX2  
2025-03-14T04:18:04.5983740Z   inflating: build/bin/test_edge_op_registration  
2025-03-14T04:18:04.6038245Z   inflating: build/bin/static_runtime_bench  
2025-03-14T04:18:04.6271082Z   inflating: build/bin/static_runtime_test  
2025-03-14T04:18:04.6344283Z   inflating: build/bin/Dict_test     
2025-03-14T04:18:04.6394820Z   inflating: build/bin/Dimname_test  
2025-03-14T04:18:04.6458252Z   inflating: build/bin/MaybeOwned_test  
2025-03-14T04:18:04.6513479Z   inflating: build/bin/NamedTensor_test  
2025-03-14T04:18:04.6573136Z   inflating: build/bin/apply_utils_test  
2025-03-14T04:18:04.6629802Z   inflating: build/bin/atest         
2025-03-14T04:18:04.6690647Z   inflating: build/bin/basic         
2025-03-14T04:18:04.6745276Z   inflating: build/bin/broadcast_test  
2025-03-14T04:18:04.6796509Z   inflating: build/bin/cpu_allocator_test  
2025-03-14T04:18:04.6852745Z   inflating: build/bin/cpu_generator_test  
2025-03-14T04:18:04.6906207Z   inflating: build/bin/cpu_profiling_allocator_test  
2025-03-14T04:18:04.6992516Z   inflating: build/bin/cpu_rng_test  
2025-03-14T04:18:04.7045281Z   inflating: build/bin/dispatch_key_set_test  
2025-03-14T04:18:04.7093397Z   inflating: build/bin/dlconvertor_test  
2025-03-14T04:18:04.7152293Z   inflating: build/bin/extension_backend_test  
2025-03-14T04:18:04.7207012Z   inflating: build/bin/half_test     
2025-03-14T04:18:04.7295523Z   inflating: build/bin/ivalue_test   
2025-03-14T04:18:04.7346922Z   inflating: build/bin/lazy_tensor_test  
2025-03-14T04:18:04.7398520Z   inflating: build/bin/math_kernel_test  
2025-03-14T04:18:04.7448779Z   inflating: build/bin/memory_format_test  
2025-03-14T04:18:04.7501956Z   inflating: build/bin/memory_overlapping_test  
2025-03-14T04:18:04.7556010Z   inflating: build/bin/mobile_memory_cleanup  
2025-03-14T04:18:04.7610693Z   inflating: build/bin/native_test   
2025-03-14T04:18:04.7663399Z   inflating: build/bin/operator_name_test  
2025-03-14T04:18:04.7732702Z   inflating: build/bin/operators_test  
2025-03-14T04:18:04.7764790Z   inflating: build/bin/packedtensoraccessor_test  
2025-03-14T04:18:04.7831848Z   inflating: build/bin/pow_test      
2025-03-14T04:18:04.7887638Z   inflating: build/bin/quantized_test  
2025-03-14T04:18:04.7938446Z   inflating: build/bin/reduce_ops_test  
2025-03-14T04:18:04.7991056Z   inflating: build/bin/reportMemoryUsage_test  
2025-03-14T04:18:04.8047870Z   inflating: build/bin/scalar_tensor_test  
2025-03-14T04:18:04.8101809Z   inflating: build/bin/scalar_test   
2025-03-14T04:18:04.8153356Z   inflating: build/bin/StorageUtils_test  
2025-03-14T04:18:04.8206165Z   inflating: build/bin/stride_properties_test  
2025-03-14T04:18:04.8282406Z   inflating: build/bin/tensor_iterator_test  
2025-03-14T04:18:04.8333748Z   inflating: build/bin/test_parallel  
2025-03-14T04:18:04.8335505Z   inflating: build/bin/thread_init_test  
2025-03-14T04:18:04.8393263Z   inflating: build/bin/type_ptr_test  
2025-03-14T04:18:04.8449985Z   inflating: build/bin/type_test     
2025-03-14T04:18:04.8505791Z   inflating: build/bin/undefined_tensor_test  
2025-03-14T04:18:04.8507801Z   inflating: build/bin/verify_api_visibility  
2025-03-14T04:18:04.8573185Z   inflating: build/bin/legacy_vmap_test  
2025-03-14T04:18:04.8625961Z   inflating: build/bin/weakref_test  
2025-03-14T04:18:04.8676008Z   inflating: build/bin/wrapdim_test  
2025-03-14T04:18:04.8728660Z   inflating: build/bin/xla_tensor_test  
2025-03-14T04:18:04.8783892Z   inflating: build/bin/IListRef_test  
2025-03-14T04:18:04.8883708Z   inflating: build/bin/List_test     
2025-03-14T04:18:04.8947814Z   inflating: build/bin/KernelFunction_test  
2025-03-14T04:18:04.9059862Z   inflating: build/bin/kernel_function_legacy_test  
2025-03-14T04:18:04.9149534Z   inflating: build/bin/kernel_function_test  
2025-03-14T04:18:04.9269066Z   inflating: build/bin/kernel_lambda_legacy_test  
2025-03-14T04:18:04.9365449Z   inflating: build/bin/kernel_lambda_test  
2025-03-14T04:18:04.9423790Z   inflating: build/bin/kernel_stackbased_test  
2025-03-14T04:18:04.9514518Z   inflating: build/bin/make_boxed_from_unboxed_functor_test  
2025-03-14T04:18:04.9566196Z   inflating: build/bin/CppSignature_test  
2025-03-14T04:18:04.9623254Z   inflating: build/bin/backend_fallback_test  
2025-03-14T04:18:04.9670882Z   inflating: build/bin/op_allowlist_test  
2025-03-14T04:18:04.9951448Z   inflating: build/bin/op_registration_test  
2025-03-14T04:18:05.0008852Z   inflating: build/bin/inline_container_test  
2025-03-14T04:18:05.0530844Z   inflating: build/bin/test_jit      
2025-03-14T04:18:05.1253254Z   inflating: build/bin/test_tensorexpr  
2025-03-14T04:18:05.1305867Z   inflating: build/bin/BackoffTest   
2025-03-14T04:18:05.1358230Z   inflating: build/bin/HashStoreTest  
2025-03-14T04:18:05.1410074Z   inflating: build/bin/FileStoreTest  
2025-03-14T04:18:05.1466486Z   inflating: build/bin/TCPStoreTest  
2025-03-14T04:18:05.1477664Z   inflating: build/bin/tutorial_tensorexpr  
2025-03-14T04:18:05.1482372Z   inflating: build/bin/example_allreduce  
2025-03-14T04:18:05.1543946Z   inflating: build/bin/ProcessGroupGlooTest  
2025-03-14T04:18:05.1597788Z   inflating: build/bin/test_dist_autograd  
2025-03-14T04:18:05.1665643Z   inflating: build/bin/test_cpp_rpc  
2025-03-14T04:18:05.2696140Z   inflating: build/bin/test_api      
2025-03-14T04:18:05.2696498Z   inflating: build/bin/parallel_benchmark  
2025-03-14T04:18:05.2762508Z   inflating: build/bin/test_mobile_nnc  
2025-03-14T04:18:05.2770184Z   inflating: build/bin/aot_model_compiler_test  
2025-03-14T04:18:05.3082327Z   inflating: build/bin/test_lazy     
2025-03-14T04:18:05.3084514Z   inflating: build/bin/torch_shm_manager  
2025-03-14T04:18:05.3084975Z    creating: .additional_ci_files/
2025-03-14T04:18:05.3181604Z   inflating: .additional_ci_files/test-times.json  
2025-03-14T04:18:05.3548666Z   inflating: .additional_ci_files/test-class-times.json  
2025-03-14T04:18:05.3575535Z ##[group]Run rm artifacts.zip
2025-03-14T04:18:05.3575827Z rm artifacts.zip
2025-03-14T04:18:05.3580348Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:18:05.3580602Z env:
2025-03-14T04:18:05.3580774Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:18:05.3580983Z ##[endgroup]
2025-03-14T04:18:05.4087422Z ##[group]Run df -H
2025-03-14T04:18:05.4087642Z df -H
2025-03-14T04:18:05.4092438Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:18:05.4092716Z env:
2025-03-14T04:18:05.4092889Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:18:05.4093082Z ##[endgroup]
2025-03-14T04:18:05.4131108Z Filesystem        Size  Used Avail Use% Mounted on
2025-03-14T04:18:05.4131722Z devtmpfs          4.2M     0  4.2M   0% /dev
2025-03-14T04:18:05.4132055Z tmpfs              67G     0   67G   0% /dev/shm
2025-03-14T04:18:05.4132309Z tmpfs              27G  783k   27G   1% /run
2025-03-14T04:18:05.4132554Z /dev/nvme0n1p1    215G   49G  167G  23% /
2025-03-14T04:18:05.4132797Z tmpfs              67G   29k   67G   1% /tmp
2025-03-14T04:18:05.4133084Z /dev/nvme0n1p128   11M  1.4M  9.2M  13% /boot/efi
2025-03-14T04:18:05.4157673Z Prepare all required actions
2025-03-14T04:18:05.4157988Z Getting action download info
2025-03-14T04:18:05.5506547Z ##[group]Run ./.github/actions/download-td-artifacts
2025-03-14T04:18:05.5506812Z with:
2025-03-14T04:18:05.5506974Z env:
2025-03-14T04:18:05.5507143Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:18:05.5507334Z ##[endgroup]
2025-03-14T04:18:05.5664139Z ##[group]Run seemethere/download-artifact-s3@v4
2025-03-14T04:18:05.5664391Z with:
2025-03-14T04:18:05.5664559Z   name: td_results
2025-03-14T04:18:05.5664777Z   s3-bucket: gha-artifacts
2025-03-14T04:18:05.5665006Z   region: us-east-1
2025-03-14T04:18:05.5665293Z env:
2025-03-14T04:18:05.5665449Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:18:05.5665635Z ##[endgroup]
2025-03-14T04:18:05.9061473Z (node:225468) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023.
2025-03-14T04:18:05.9067199Z 
2025-03-14T04:18:05.9071647Z Please migrate your code to use AWS SDK for JavaScript (v3).
2025-03-14T04:18:05.9072089Z For more information, check the migration guide at https://a.co/7PzMCcy
2025-03-14T04:18:05.9072445Z (Use `node --trace-warnings ...` to show where the warning was created)
2025-03-14T04:18:05.9832024Z Found 0 objects with prefix pytorch/pytorch/13849515380/td_results/
2025-03-14T04:18:05.9838585Z Artifact download has finished successfully
2025-03-14T04:18:06.0237715Z ##[group]Run mkdir -p .additional_ci_files
2025-03-14T04:18:06.0238021Z mkdir -p .additional_ci_files
2025-03-14T04:18:06.0238304Z mv td_results.json .additional_ci_files/td_results.json || true
2025-03-14T04:18:06.0243305Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:18:06.0243555Z env:
2025-03-14T04:18:06.0243714Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:18:06.0243903Z ##[endgroup]
2025-03-14T04:18:06.0293244Z mv: cannot stat 'td_results.json': No such file or directory
2025-03-14T04:18:06.0577704Z ##[group]Run .github/scripts/parse_ref.py
2025-03-14T04:18:06.0577979Z .github/scripts/parse_ref.py
2025-03-14T04:18:06.0582637Z shell: /usr/bin/bash -e {0}
2025-03-14T04:18:06.0582833Z env:
2025-03-14T04:18:06.0582999Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:18:06.0583190Z ##[endgroup]
2025-03-14T04:18:06.0839405Z Prepare all required actions
2025-03-14T04:18:06.0840345Z Getting action download info
2025-03-14T04:18:06.2181193Z ##[group]Run ./.github/actions/filter-test-configs
2025-03-14T04:18:06.2181457Z with:
2025-03-14T04:18:06.2181846Z   github-token: ***
2025-03-14T04:18:06.2183480Z   test-matrix: {"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]}
2025-03-14T04:18:06.2185252Z   job-name: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:18:06.2185620Z env:
2025-03-14T04:18:06.2185791Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:18:06.2185984Z ##[endgroup]
2025-03-14T04:18:06.2339762Z ##[group]Run nick-fields/retry@v3.0.0
2025-03-14T04:18:06.2339988Z with:
2025-03-14T04:18:06.2340156Z   shell: bash
2025-03-14T04:18:06.2340325Z   timeout_minutes: 10
2025-03-14T04:18:06.2340500Z   max_attempts: 5
2025-03-14T04:18:06.2340672Z   retry_wait_seconds: 30
2025-03-14T04:18:06.2341146Z   command: set -eux
# PyYAML 6.0 doesn't work with MacOS x86 anymore
# This must run on Python-3.7 (AmazonLinux2) so can't use request=3.32.2
python3 -m pip install requests==2.27.1 pyyaml==6.0.1

2025-03-14T04:18:06.2341620Z   polling_interval_seconds: 1
2025-03-14T04:18:06.2341825Z   warning_on_retry: true
2025-03-14T04:18:06.2342035Z   continue_on_error: false
2025-03-14T04:18:06.2342210Z env:
2025-03-14T04:18:06.2342364Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:18:06.2342885Z   GITHUB_TOKEN: ***
2025-03-14T04:18:06.2343062Z ##[endgroup]
2025-03-14T04:18:06.3147953Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1
2025-03-14T04:18:06.4845069Z Defaulting to user installation because normal site-packages is not writeable
2025-03-14T04:18:06.4982152Z Requirement already satisfied: requests==2.27.1 in /home/ec2-user/.local/lib/python3.9/site-packages (2.27.1)
2025-03-14T04:18:06.4982907Z Requirement already satisfied: pyyaml==6.0.1 in /home/ec2-user/.local/lib/python3.9/site-packages (6.0.1)
2025-03-14T04:18:06.5067025Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10)
2025-03-14T04:18:06.5067824Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2025.1.31)
2025-03-14T04:18:06.5070507Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10)
2025-03-14T04:18:06.5077070Z Requirement already satisfied: charset-normalizer~=2.0.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2.0.12)
2025-03-14T04:18:07.2959750Z Command completed after 1 attempt(s).
2025-03-14T04:18:07.3123704Z ##[group]Run set -x
2025-03-14T04:18:07.3123945Z set -x
2025-03-14T04:18:07.3124120Z 
2025-03-14T04:18:07.3124386Z # Use relative path here as this could be checked out anywhere, not necessarily
2025-03-14T04:18:07.3124694Z # in runner workspace
2025-03-14T04:18:07.3124961Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py"
2025-03-14T04:18:07.3130085Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:18:07.3130354Z env:
2025-03-14T04:18:07.3130533Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:18:07.3130871Z ##[endgroup]
2025-03-14T04:18:07.3153595Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py
2025-03-14T04:18:07.3462615Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}"
2025-03-14T04:18:07.3462910Z echo "Workflow: ${GITHUB_WORKFLOW}"
2025-03-14T04:18:07.3463143Z echo "Job name: ${JOB_NAME}"
2025-03-14T04:18:07.3463360Z 
2025-03-14T04:18:07.3463611Z # Use relative path here as this could be checked out anywhere, not necessarily
2025-03-14T04:18:07.3463913Z # in runner workspace
2025-03-14T04:18:07.3464189Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \
2025-03-14T04:18:07.3464490Z   --workflow "${GITHUB_WORKFLOW}" \
2025-03-14T04:18:07.3464715Z   --job-name "${JOB_NAME}" \
2025-03-14T04:18:07.3466310Z   --test-matrix "{"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]}" \
2025-03-14T04:18:07.3467929Z   --selected-test-configs "" \
2025-03-14T04:18:07.3468156Z   --pr-number "${PR_NUMBER}" \
2025-03-14T04:18:07.3468371Z   --tag "${TAG}" \
2025-03-14T04:18:07.3468582Z   --event-name "${EVENT_NAME}" \
2025-03-14T04:18:07.3468804Z   --schedule "${SCHEDULE}" \
2025-03-14T04:18:07.3469019Z   --branch "${HEAD_BRANCH}"
2025-03-14T04:18:07.3473437Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:18:07.3473697Z env:
2025-03-14T04:18:07.3473867Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:18:07.3474253Z   GITHUB_TOKEN: ***
2025-03-14T04:18:07.3474733Z   JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:18:07.3475102Z   PR_NUMBER: 
2025-03-14T04:18:07.3475270Z   TAG: 
2025-03-14T04:18:07.3475415Z   EVENT_NAME: push
2025-03-14T04:18:07.3475584Z   SCHEDULE: 
2025-03-14T04:18:07.3475743Z   HEAD_BRANCH: 
2025-03-14T04:18:07.3475908Z ##[endgroup]
2025-03-14T04:18:07.3500524Z Workflow: inductor
2025-03-14T04:18:07.3504929Z Job name: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:18:07.5103090Z ##[group]Run echo "Filtered matrix:"
2025-03-14T04:18:07.5103363Z echo "Filtered matrix:"
2025-03-14T04:18:07.5104959Z echo "{"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]}"
2025-03-14T04:18:07.5106693Z 
2025-03-14T04:18:07.5106862Z echo
2025-03-14T04:18:07.5107073Z echo "Is the current job unstable? False"
2025-03-14T04:18:07.5107305Z 
2025-03-14T04:18:07.5107461Z echo
2025-03-14T04:18:07.5107658Z echo "Is keep-going label set? False"
2025-03-14T04:18:07.5107883Z 
2025-03-14T04:18:07.5108033Z echo
2025-03-14T04:18:07.5108212Z echo "Renabled issues? "
2025-03-14T04:18:07.5112565Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:18:07.5112827Z env:
2025-03-14T04:18:07.5113000Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:18:07.5113192Z ##[endgroup]
2025-03-14T04:18:07.5140767Z Filtered matrix:
2025-03-14T04:18:07.5142451Z {include: [{config: cpu_inductor_torchbench, shard: 1, num_shards: 2, runner: linux.8xlarge.amx}, {config: cpu_inductor_torchbench, shard: 2, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_huggingface, shard: 1, num_shards: 1, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_timm, shard: 1, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_timm, shard: 2, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_torchbench, shard: 1, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_torchbench, shard: 2, num_shards: 2, runner: linux.8xlarge.amx}, {config: inductor_torchbench_cpu_smoketest_perf, shard: 1, num_shards: 1, runner: linux.24xl.spr-metal}]}
2025-03-14T04:18:07.5144018Z 
2025-03-14T04:18:07.5144107Z Is the current job unstable? False
2025-03-14T04:18:07.5144279Z 
2025-03-14T04:18:07.5144385Z Is keep-going label set? False
2025-03-14T04:18:07.5144547Z 
2025-03-14T04:18:07.5144631Z Renabled issues? 
2025-03-14T04:18:07.5261788Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
2025-03-14T04:18:07.5262161Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
2025-03-14T04:18:07.5266427Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:18:07.5266683Z env:
2025-03-14T04:18:07.5266853Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:18:07.5267040Z   JOB_TIMEOUT: 240
2025-03-14T04:18:07.5267201Z ##[endgroup]
2025-03-14T04:18:07.5448910Z ##[group]Run set -x
2025-03-14T04:18:07.5449180Z set -x
2025-03-14T04:18:07.5449345Z 
2025-03-14T04:18:07.5449527Z if [[ $TEST_CONFIG == 'multigpu' ]]; then
2025-03-14T04:18:07.5449904Z   TEST_COMMAND=.ci/pytorch/multigpu-test.sh
2025-03-14T04:18:07.5450169Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then
2025-03-14T04:18:07.5450417Z   TEST_COMMAND=.ci/onnx/test.sh
2025-03-14T04:18:07.5450628Z else
2025-03-14T04:18:07.5450820Z   TEST_COMMAND=.ci/pytorch/test.sh
2025-03-14T04:18:07.5451037Z fi
2025-03-14T04:18:07.5451192Z 
2025-03-14T04:18:07.5451379Z # Leaving 1GB for the runner and other things
2025-03-14T04:18:07.5451899Z TOTAL_AVAILABLE_MEMORY_IN_GB=$(awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo)
2025-03-14T04:18:07.5452496Z # https://docs.docker.com/engine/containers/resource_constraints/#--memory-swap-details, the 3GB swap
2025-03-14T04:18:07.5452926Z # comes from https://github.com/pytorch/test-infra/pull/6058
2025-03-14T04:18:07.5453265Z TOTAL_MEMORY_WITH_SWAP=$(("${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}" + 3))
2025-03-14T04:18:07.5453525Z 
2025-03-14T04:18:07.5453711Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then
2025-03-14T04:18:07.5453932Z   SHM_OPTS=
2025-03-14T04:18:07.5454105Z   JENKINS_USER=
2025-03-14T04:18:07.5454336Z   # ensure that docker container cleanly exits in 12 hours
2025-03-14T04:18:07.5454621Z   # if for some reason cleanup action doesn't stop container
2025-03-14T04:18:07.5454869Z   # when job is cancelled
2025-03-14T04:18:07.5455079Z   DOCKER_SHELL_CMD="sleep 12h"
2025-03-14T04:18:07.5455279Z 
2025-03-14T04:18:07.5455518Z   # since some steps are skipped on s390x, if they are necessary, run them here
2025-03-14T04:18:07.5455847Z   env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-14T04:18:07.5456126Z   env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-14T04:18:07.5456351Z else
2025-03-14T04:18:07.5456532Z   SHM_OPTS="--shm-size=${SHM_SIZE}"
2025-03-14T04:18:07.5456759Z   JENKINS_USER="--user jenkins"
2025-03-14T04:18:07.5456967Z   DOCKER_SHELL_CMD=
2025-03-14T04:18:07.5457150Z fi
2025-03-14T04:18:07.5457299Z 
2025-03-14T04:18:07.5457521Z # detached container should get cleaned up by teardown_ec2_linux
2025-03-14T04:18:07.5457851Z # TODO: Stop building test binaries as part of the build phase
2025-03-14T04:18:07.5458200Z # Used for GPU_FLAG, SHM_OPTS, JENKINS_USER and DOCKER_SHELL_CMD since that doesn't play nice
2025-03-14T04:18:07.5458520Z # shellcheck disable=SC2086,SC2090
2025-03-14T04:18:07.5458742Z container_name=$(docker run \
2025-03-14T04:18:07.5458963Z   ${GPU_FLAG:-} \
2025-03-14T04:18:07.5459170Z   ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \
2025-03-14T04:18:07.5459396Z   -e BUILD_ENVIRONMENT \
2025-03-14T04:18:07.5459596Z   -e PR_NUMBER \
2025-03-14T04:18:07.5459791Z   -e GITHUB_ACTIONS \
2025-03-14T04:18:07.5459980Z   -e GITHUB_REPOSITORY \
2025-03-14T04:18:07.5460182Z   -e GITHUB_WORKFLOW \
2025-03-14T04:18:07.5460377Z   -e GITHUB_JOB \
2025-03-14T04:18:07.5460996Z   -e GITHUB_RUN_ID \
2025-03-14T04:18:07.5461201Z   -e GITHUB_RUN_NUMBER \
2025-03-14T04:18:07.5461405Z   -e GITHUB_RUN_ATTEMPT \
2025-03-14T04:18:07.5461610Z   -e JOB_ID \
2025-03-14T04:18:07.5461791Z   -e JOB_NAME \
2025-03-14T04:18:07.5461972Z   -e BASE_SHA \
2025-03-14T04:18:07.5462153Z   -e BRANCH \
2025-03-14T04:18:07.5462330Z   -e SHA1 \
2025-03-14T04:18:07.5462508Z   -e AWS_DEFAULT_REGION \
2025-03-14T04:18:07.5462824Z   -e IN_WHEEL_TEST \
2025-03-14T04:18:07.5463023Z   -e SHARD_NUMBER \
2025-03-14T04:18:07.5463215Z   -e TEST_CONFIG \
2025-03-14T04:18:07.5463411Z   -e NUM_TEST_SHARDS \
2025-03-14T04:18:07.5463684Z   -e REENABLED_ISSUES \
2025-03-14T04:18:07.5463881Z   -e CONTINUE_THROUGH_ERROR \
2025-03-14T04:18:07.5464078Z   -e VERBOSE_TEST_LOGS \
2025-03-14T04:18:07.5464279Z   -e TEST_SHOWLOCALS \
2025-03-14T04:18:07.5464479Z   -e NO_TEST_TIMEOUT \
2025-03-14T04:18:07.5464672Z   -e NO_TD \
2025-03-14T04:18:07.5464853Z   -e TD_DISTRIBUTED \
2025-03-14T04:18:07.5465046Z   -e PR_LABELS \
2025-03-14T04:18:07.5465249Z   -e MAX_JOBS="$(nproc --ignore=2)" \
2025-03-14T04:18:07.5465482Z   -e SCCACHE_BUCKET \
2025-03-14T04:18:07.5465675Z   -e SCCACHE_REGION \
2025-03-14T04:18:07.5465866Z   -e XLA_CUDA \
2025-03-14T04:18:07.5466062Z   -e XLA_CLANG_CACHE_S3_BUCKET_NAME \
2025-03-14T04:18:07.5466302Z   -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \
2025-03-14T04:18:07.5466547Z   -e PYTORCH_TEST_RERUN_DISABLED_TESTS \
2025-03-14T04:18:07.5466786Z   -e SKIP_SCCACHE_INITIALIZATION=1 \
2025-03-14T04:18:07.5467014Z   -e HUGGING_FACE_HUB_TOKEN \
2025-03-14T04:18:07.5467229Z   -e SCRIBE_GRAPHQL_ACCESS_TOKEN \
2025-03-14T04:18:07.5467474Z   -e DASHBOARD_TAG \
2025-03-14T04:18:07.5467666Z   -e IS_A100_RUNNER \
2025-03-14T04:18:07.5467868Z   -e ARTIFACTS_FILE_SUFFIX \
2025-03-14T04:18:07.5468111Z   --memory="${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}g" \
2025-03-14T04:18:07.5468384Z   --memory-swap="${TOTAL_MEMORY_WITH_SWAP}g" \
2025-03-14T04:18:07.5468653Z   --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
2025-03-14T04:18:07.5468906Z   --security-opt seccomp=unconfined \
2025-03-14T04:18:07.5469127Z   --cap-add=SYS_PTRACE \
2025-03-14T04:18:07.5469333Z   --ipc=host \
2025-03-14T04:18:07.5469517Z   ${SHM_OPTS} \
2025-03-14T04:18:07.5469687Z   --tty \
2025-03-14T04:18:07.5469856Z   --detach \
2025-03-14T04:18:07.5470040Z   --name="${container_name}" \
2025-03-14T04:18:07.5470254Z   ${JENKINS_USER} \
2025-03-14T04:18:07.5470487Z   -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
2025-03-14T04:18:07.5470740Z   -w /var/lib/jenkins/workspace \
2025-03-14T04:18:07.5470948Z   "${DOCKER_IMAGE}" \
2025-03-14T04:18:07.5471136Z   ${DOCKER_SHELL_CMD}
2025-03-14T04:18:07.5471318Z )
2025-03-14T04:18:07.5471514Z # Propagate download.pytorch.org IP to container
2025-03-14T04:18:07.5471912Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts"
2025-03-14T04:18:07.5472333Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}"
2025-03-14T04:18:07.5472590Z 
2025-03-14T04:18:07.5472781Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then
2025-03-14T04:18:07.5473134Z   docker exec -t "${container_name}" sh -c "python3 -m pip install -r .ci/docker/requirements-ci.txt"
2025-03-14T04:18:07.5473449Z fi
2025-03-14T04:18:07.5473602Z 
2025-03-14T04:18:07.5473903Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}"
2025-03-14T04:18:07.5478069Z shell: /usr/bin/bash -e {0}
2025-03-14T04:18:07.5478266Z env:
2025-03-14T04:18:07.5478435Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:18:07.5478660Z   BUILD_ENVIRONMENT: linux-jammy-py3.9-gcc11-build
2025-03-14T04:18:07.5478895Z   PR_NUMBER: 
2025-03-14T04:18:07.5479073Z   GITHUB_REPOSITORY: pytorch/pytorch
2025-03-14T04:18:07.5479289Z   GITHUB_WORKFLOW: inductor
2025-03-14T04:18:07.5479474Z   GITHUB_JOB: test
2025-03-14T04:18:07.5479637Z   GITHUB_RUN_ID: 13849515380
2025-03-14T04:18:07.5479896Z   GITHUB_RUN_NUMBER: 122697
2025-03-14T04:18:07.5480082Z   GITHUB_RUN_ATTEMPT: 1
2025-03-14T04:18:07.5480259Z   JOB_ID: 38754842362
2025-03-14T04:18:07.5480606Z   JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:18:07.5481006Z   BRANCH: main
2025-03-14T04:18:07.5481192Z   SHA1: aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:18:07.5481438Z   BASE_SHA: aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:18:07.5481684Z   TEST_CONFIG: dynamic_cpu_inductor_torchbench
2025-03-14T04:18:07.5481900Z   SHARD_NUMBER: 1
2025-03-14T04:18:07.5482066Z   NUM_TEST_SHARDS: 2
2025-03-14T04:18:07.5482238Z   REENABLED_ISSUES: 
2025-03-14T04:18:07.5482415Z   CONTINUE_THROUGH_ERROR: False
2025-03-14T04:18:07.5482609Z   VERBOSE_TEST_LOGS: False
2025-03-14T04:18:07.5482794Z   TEST_SHOWLOCALS: False
2025-03-14T04:18:07.5482973Z   NO_TEST_TIMEOUT: False
2025-03-14T04:18:07.5483147Z   NO_TD: False
2025-03-14T04:18:07.5483308Z   TD_DISTRIBUTED: False
2025-03-14T04:18:07.5483510Z   SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2
2025-03-14T04:18:07.5483749Z   SCCACHE_REGION: us-east-1
2025-03-14T04:18:07.5483930Z   SHM_SIZE: 1g
2025-03-14T04:18:07.5484376Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:18:07.5484847Z   XLA_CUDA: 
2025-03-14T04:18:07.5485088Z   XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla
2025-03-14T04:18:07.5485373Z   PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0
2025-03-14T04:18:07.5485589Z   PYTORCH_TEST_RERUN_DISABLED_TESTS: 0
2025-03-14T04:18:07.5485792Z   DASHBOARD_TAG: 
2025-03-14T04:18:07.5486125Z   HUGGING_FACE_HUB_TOKEN: ***
2025-03-14T04:18:07.5486408Z   SCRIBE_GRAPHQL_ACCESS_TOKEN: ***
2025-03-14T04:18:07.5486611Z   IS_A100_RUNNER: 0
2025-03-14T04:18:07.5486905Z   ARTIFACTS_FILE_SUFFIX: test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754842362
2025-03-14T04:18:07.5487228Z ##[endgroup]
2025-03-14T04:18:07.5509127Z + [[ dynamic_cpu_inductor_torchbench == \m\u\l\t\i\g\p\u ]]
2025-03-14T04:18:07.5513975Z + [[ linux-jammy-py3.9-gcc11-build == *onnx* ]]
2025-03-14T04:18:07.5515977Z + TEST_COMMAND=.ci/pytorch/test.sh
2025-03-14T04:18:07.5516429Z ++ awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo
2025-03-14T04:18:07.5530942Z + TOTAL_AVAILABLE_MEMORY_IN_GB='122.780 '
2025-03-14T04:18:07.5531231Z + TOTAL_MEMORY_WITH_SWAP=125
2025-03-14T04:18:07.5531486Z + [[ linux-jammy-py3.9-gcc11-build == *\s\3\9\0\x* ]]
2025-03-14T04:18:07.5531866Z + SHM_OPTS=--shm-size=1g
2025-03-14T04:18:07.5532071Z + JENKINS_USER='--user jenkins'
2025-03-14T04:18:07.5532277Z + DOCKER_SHELL_CMD=
2025-03-14T04:18:07.5541331Z +++ nproc --ignore=2
2025-03-14T04:18:07.5552011Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e GITHUB_REPOSITORY -e GITHUB_WORKFLOW -e GITHUB_JOB -e GITHUB_RUN_ID -e GITHUB_RUN_NUMBER -e GITHUB_RUN_ATTEMPT -e JOB_ID -e JOB_NAME -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e REENABLED_ISSUES -e CONTINUE_THROUGH_ERROR -e VERBOSE_TEST_LOGS -e TEST_SHOWLOCALS -e NO_TEST_TIMEOUT -e NO_TD -e TD_DISTRIBUTED -e PR_LABELS -e MAX_JOBS=30 -e SCCACHE_BUCKET -e SCCACHE_REGION -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -e SKIP_SCCACHE_INITIALIZATION=1 -e HUGGING_FACE_HUB_TOKEN -e SCRIBE_GRAPHQL_ACCESS_TOKEN -e DASHBOARD_TAG -e IS_A100_RUNNER -e ARTIFACTS_FILE_SUFFIX --memory=122g --memory-swap=125g --env-file=/tmp/github_env_13849515380 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --shm-size=1g --tty --detach --name= --user jenkins -v /home/ec2-user/actions-runner/_work/pytorch/pytorch:/var/lib/jenkins/workspace -w /var/lib/jenkins/workspace 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:18:55.9391031Z + container_name=f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T04:18:55.9393203Z + grep download.pytorch.org /etc/hosts
2025-03-14T04:18:55.9400684Z + docker exec -i f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05 sudo bash -c '/bin/cat >> /etc/hosts'
2025-03-14T04:18:56.1037764Z + echo DOCKER_CONTAINER_ID=f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T04:18:56.1038241Z + [[ linux-jammy-py3.9-gcc11-build == *\s\3\9\0\x* ]]
2025-03-14T04:18:56.1042190Z ++ echo dist/torch-2.8.0a0+gitaed0b7a-cp39-cp39-linux_x86_64.whl
2025-03-14T04:18:56.1043915Z + docker exec -t f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05 sh -c 'python3 -m pip install dist/torch-2.8.0a0+gitaed0b7a-cp39-cp39-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh'
2025-03-14T04:18:56.4118650Z Processing ./dist/torch-2.8.0a0+gitaed0b7a-cp39-cp39-linux_x86_64.whl (from torch==2.8.0a0+gitaed0b7a)
2025-03-14T04:18:56.5951676Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (3.16.1)
2025-03-14T04:18:56.5952634Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (4.12.2)
2025-03-14T04:18:56.6159355Z Collecting sympy>=1.13.3 (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a)
2025-03-14T04:18:56.6173788Z   Using cached sympy-1.13.3-py3-none-any.whl.metadata (12 kB)
2025-03-14T04:18:56.6187974Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (2.8.8)
2025-03-14T04:18:56.6188739Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (3.1.6)
2025-03-14T04:18:56.6189475Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (2024.10.0)
2025-03-14T04:18:56.6206141Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (3.3.0)
2025-03-14T04:18:56.6222341Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from opt-einsum>=3.3->torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (1.22.4)
2025-03-14T04:18:56.6226568Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from sympy>=1.13.3->torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (1.3.0)
2025-03-14T04:18:56.6502836Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from jinja2->torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (3.0.2)
2025-03-14T04:18:56.6566568Z Using cached sympy-1.13.3-py3-none-any.whl (6.2 MB)
2025-03-14T04:18:57.1816208Z Installing collected packages: sympy, torch
2025-03-14T04:18:57.1817356Z   Attempting uninstall: sympy
2025-03-14T04:18:57.1823669Z     Found existing installation: sympy 1.13.1
2025-03-14T04:18:57.2726107Z     Uninstalling sympy-1.13.1:
2025-03-14T04:18:57.7514912Z       Successfully uninstalled sympy-1.13.1
2025-03-14T04:19:06.9554511Z ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
2025-03-14T04:19:06.9555178Z timm 1.0.14 requires torchvision, which is not installed.
2025-03-14T04:19:06.9555562Z Successfully installed sympy-1.13.3 torch-2.8.0a0+gitaed0b7a
2025-03-14T04:19:07.0407110Z + export TERM=vt100
2025-03-14T04:19:07.0407392Z + TERM=vt100
2025-03-14T04:19:07.0407582Z ++ dirname .ci/pytorch/test.sh
2025-03-14T04:19:07.0407816Z + source .ci/pytorch/common.sh
2025-03-14T04:19:07.0410717Z +++ dirname .ci/pytorch/common.sh
2025-03-14T04:19:07.0420767Z ++ source .ci/pytorch/common_utils.sh
2025-03-14T04:19:07.0421067Z +++ declare -f -t trap_add
2025-03-14T04:19:07.0426359Z ++ set -ex -o pipefail
2025-03-14T04:19:07.0426595Z ++ [[ linux-jammy-py3.9-gcc11-build == *rocm* ]]
2025-03-14T04:19:07.0426963Z ++ BUILD_TEST_LIBTORCH=0
2025-03-14T04:19:07.0427191Z + [[ linux-jammy-py3.9-gcc11-build != *rocm* ]]
2025-03-14T04:19:07.0427454Z + [[ linux-jammy-py3.9-gcc11-build != *s390x* ]]
2025-03-14T04:19:07.0427706Z + [[ -d /var/lib/jenkins/workspace ]]
2025-03-14T04:19:07.0432076Z ++ stat -c %u /var/lib/jenkins/workspace
2025-03-14T04:19:07.0442077Z + WORKSPACE_ORIGINAL_OWNER_ID=1000
2025-03-14T04:19:07.0442385Z + trap_add cleanup_workspace EXIT
2025-03-14T04:19:07.0442662Z + trap_add_cmd=cleanup_workspace
2025-03-14T04:19:07.0442895Z + shift
2025-03-14T04:19:07.0443113Z + for trap_add_name in "$@"
2025-03-14T04:19:07.0449147Z +++ trap -p EXIT
2025-03-14T04:19:07.0449362Z ++ eval 'extract_trap_cmd '
2025-03-14T04:19:07.0449573Z +++ extract_trap_cmd
2025-03-14T04:19:07.0449822Z +++ printf '%s\n' ''
2025-03-14T04:19:07.0450028Z ++ printf '%s\n' cleanup_workspace
2025-03-14T04:19:07.0452180Z + trap -- '
2025-03-14T04:19:07.0452377Z cleanup_workspace' EXIT
2025-03-14T04:19:07.0454771Z + sudo chown -R jenkins /var/lib/jenkins/workspace
2025-03-14T04:19:07.4027793Z + git config --global --add safe.directory /var/lib/jenkins/workspace
2025-03-14T04:19:07.4046642Z + echo 'Environment variables:'
2025-03-14T04:19:07.4047080Z Environment variables:
2025-03-14T04:19:07.4047373Z + env
2025-03-14T04:19:07.4057129Z INSTALLED_DB=yes
2025-03-14T04:19:07.4062345Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-14T04:19:07.4064162Z CONTINUE_THROUGH_ERROR=False
2025-03-14T04:19:07.4064531Z BUILD_ENVIRONMENT=linux-jammy-py3.9-gcc11-build
2025-03-14T04:19:07.4064788Z HOSTNAME=f121771cb12d
2025-03-14T04:19:07.4065179Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_cf747ae1-d501-4db5-a2cf-d8c5918f871b
2025-03-14T04:19:07.4065627Z GITHUB_ACTION=__self
2025-03-14T04:19:07.4065833Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2025-03-14T04:19:07.4066055Z GITHUB_RUN_NUMBER=122697
2025-03-14T04:19:07.4066267Z TEST_CONFIG=dynamic_cpu_inductor_torchbench
2025-03-14T04:19:07.4066515Z GITHUB_REPOSITORY_OWNER_ID=21003710
2025-03-14T04:19:07.4066745Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2025-03-14T04:19:07.4066962Z IS_A100_RUNNER=0
2025-03-14T04:19:07.4067397Z SCRIBE_GRAPHQL_ACCESS_TOKEN=***
2025-03-14T04:19:07.4067620Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot
2025-03-14T04:19:07.4067830Z GITHUB_REF_TYPE=branch
2025-03-14T04:19:07.4068023Z TORCH_CUDA_ARCH_LIST=Maxwell
2025-03-14T04:19:07.4068249Z BASE_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:19:07.4068477Z XLA_CUDA=
2025-03-14T04:19:07.4068711Z HUGGING_FACE_HUB_TOKEN=***
2025-03-14T04:19:07.4072148Z ***
2025-03-14T04:19:07.4072363Z GITHUB_REPOSITORY_ID=65600975
2025-03-14T04:19:07.4072579Z GITHUB_ACTIONS=true
2025-03-14T04:19:07.4072782Z SHA1=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:19:07.4073057Z GITHUB_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:19:07.4073390Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/inductor.yml@refs/heads/main
2025-03-14T04:19:07.4073697Z UCC_HOME=/usr
2025-03-14T04:19:07.4073862Z VERBOSE_TEST_LOGS=False
2025-03-14T04:19:07.4074044Z GITHUB_REF=refs/heads/main
2025-03-14T04:19:07.4074225Z SHARD_NUMBER=1
2025-03-14T04:19:07.4074392Z GITHUB_REF_PROTECTED=true
2025-03-14T04:19:07.4074575Z HOME=/var/lib/jenkins
2025-03-14T04:19:07.4074775Z GITHUB_API_URL=https://api.github.com
2025-03-14T04:19:07.4075004Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2025-03-14T04:19:07.4075195Z UCX_COMMIT=
2025-03-14T04:19:07.4075349Z NUM_TEST_SHARDS=2
2025-03-14T04:19:07.4075511Z UCX_HOME=/usr
2025-03-14T04:19:07.4075858Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_cf747ae1-d501-4db5-a2cf-d8c5918f871b
2025-03-14T04:19:07.4076615Z JOB_NAME=linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:19:07.4077148Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_cf747ae1-d501-4db5-a2cf-d8c5918f871b
2025-03-14T04:19:07.4077613Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2025-03-14T04:19:07.4077999Z GITHUB_EVENT_NAME=push
2025-03-14T04:19:07.4078178Z DASHBOARD_TAG=
2025-03-14T04:19:07.4078342Z GITHUB_RUN_ID=13849515380
2025-03-14T04:19:07.4078716Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_cf747ae1-d501-4db5-a2cf-d8c5918f871b
2025-03-14T04:19:07.4079109Z GITHUB_ACTOR=pytorchmergebot
2025-03-14T04:19:07.4079301Z PR_NUMBER=
2025-03-14T04:19:07.4079456Z DESIRED_CUDA=
2025-03-14T04:19:07.4079617Z GITHUB_RUN_ATTEMPT=1
2025-03-14T04:19:07.4079795Z ANACONDA_PYTHON_VERSION=3.9
2025-03-14T04:19:07.4080019Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2025-03-14T04:19:07.4080243Z TERM=vt100
2025-03-14T04:19:07.4080395Z INSTALLED_VISION=yes
2025-03-14T04:19:07.4080572Z BRANCH=main
2025-03-14T04:19:07.4080737Z SCCACHE_REGION=us-east-1
2025-03-14T04:19:07.4080933Z OPENSSL_ROOT_DIR=/opt/openssl
2025-03-14T04:19:07.4081133Z CUDA_PATH=/usr/local/cuda
2025-03-14T04:19:07.4081464Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2025-03-14T04:19:07.4081849Z GITHUB_SERVER_URL=https://github.com
2025-03-14T04:19:07.4082053Z UCC_COMMIT=
2025-03-14T04:19:07.4082209Z REENABLED_ISSUES=
2025-03-14T04:19:07.4082384Z DOCS=yes
2025-03-14T04:19:07.4082534Z SHLVL=1
2025-03-14T04:19:07.4082684Z MAX_JOBS=30
2025-03-14T04:19:07.4082844Z GITHUB_ACTOR_ID=97764156
2025-03-14T04:19:07.4083086Z GITHUB_WORKFLOW_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:19:07.4083334Z GITHUB_REF_NAME=main
2025-03-14T04:19:07.4083589Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2025-03-14T04:19:07.4083858Z GITHUB_JOB=test
2025-03-14T04:19:07.4084026Z NO_TEST_TIMEOUT=False
2025-03-14T04:19:07.4084196Z TD_DISTRIBUTED=False
2025-03-14T04:19:07.4084389Z GITHUB_REPOSITORY=pytorch/pytorch
2025-03-14T04:19:07.4084595Z GITHUB_RETENTION_DAYS=90
2025-03-14T04:19:07.4084781Z OPENSSL_DIR=/opt/openssl
2025-03-14T04:19:07.4084972Z GITHUB_ACTION_REPOSITORY=
2025-03-14T04:19:07.4085439Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-14T04:19:07.4085905Z GITHUB_BASE_REF=
2025-03-14T04:19:07.4086071Z INSTALLED_ACL=
2025-03-14T04:19:07.4086363Z ARTIFACTS_FILE_SUFFIX=test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754842362
2025-03-14T04:19:07.4086682Z CI=true
2025-03-14T04:19:07.4086847Z GITHUB_REPOSITORY_OWNER=pytorch
2025-03-14T04:19:07.4087038Z JOB_ID=38754842362
2025-03-14T04:19:07.4087208Z INSTALLED_PROTOBUF=yes
2025-03-14T04:19:07.4087381Z GITHUB_HEAD_REF=
2025-03-14T04:19:07.4087545Z GITHUB_ACTION_REF=
2025-03-14T04:19:07.4087746Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2025-03-14T04:19:07.4087976Z TEST_SHOWLOCALS=False
2025-03-14T04:19:07.4088155Z GITHUB_WORKFLOW=inductor
2025-03-14T04:19:07.4088342Z DEBIAN_FRONTEND=noninteractive
2025-03-14T04:19:07.4088726Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_cf747ae1-d501-4db5-a2cf-d8c5918f871b
2025-03-14T04:19:07.4089109Z NO_TD=False
2025-03-14T04:19:07.4089278Z SKIP_SCCACHE_INITIALIZATION=1
2025-03-14T04:19:07.4089471Z _=/usr/bin/env
2025-03-14T04:19:07.4089694Z ++ python -c 'import site; print(site.getsitepackages()[0])'
2025-03-14T04:19:07.4322418Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch
2025-03-14T04:19:07.4324919Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/bin
2025-03-14T04:19:07.4325319Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib
2025-03-14T04:19:07.4325685Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/test
2025-03-14T04:19:07.4326311Z + BUILD_DIR=build
2025-03-14T04:19:07.4326520Z + BUILD_RENAMED_DIR=build_renamed
2025-03-14T04:19:07.4326738Z + BUILD_BIN_DIR=build/bin
2025-03-14T04:19:07.4326932Z + SHARD_NUMBER=1
2025-03-14T04:19:07.4327116Z + NUM_TEST_SHARDS=2
2025-03-14T04:19:07.4327415Z + export TORCH_SERIALIZATION_DEBUG=1
2025-03-14T04:19:07.4327634Z + TORCH_SERIALIZATION_DEBUG=1
2025-03-14T04:19:07.4327838Z + export VALGRIND=ON
2025-03-14T04:19:07.4328020Z + VALGRIND=ON
2025-03-14T04:19:07.4328259Z + [[ linux-jammy-py3.9-gcc11-build == *clang9* ]]
2025-03-14T04:19:07.4328529Z + [[ linux-jammy-py3.9-gcc11-build == *xpu* ]]
2025-03-14T04:19:07.4328779Z + [[ linux-jammy-py3.9-gcc11-build == *s390x* ]]
2025-03-14T04:19:07.4329004Z + [[ 0 == \1 ]]
2025-03-14T04:19:07.4329175Z + [[ False == \1 ]]
2025-03-14T04:19:07.4329374Z + [[ linux-jammy-py3.9-gcc11-build != *bazel* ]]
2025-03-14T04:19:07.4329627Z ++ realpath build/custom_test_artifacts
2025-03-14T04:19:07.4343045Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts
2025-03-14T04:19:07.4343534Z + [[ -n '' ]]
2025-03-14T04:19:07.4343815Z + echo 'Environment variables'
2025-03-14T04:19:07.4344162Z Environment variables
2025-03-14T04:19:07.4344386Z + env
2025-03-14T04:19:07.4345260Z INSTALLED_DB=yes
2025-03-14T04:19:07.4345778Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-14T04:19:07.4346216Z CONTINUE_THROUGH_ERROR=False
2025-03-14T04:19:07.4346599Z BUILD_ENVIRONMENT=linux-jammy-py3.9-gcc11-build
2025-03-14T04:19:07.4346860Z HOSTNAME=f121771cb12d
2025-03-14T04:19:07.4347259Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_cf747ae1-d501-4db5-a2cf-d8c5918f871b
2025-03-14T04:19:07.4347658Z GITHUB_ACTION=__self
2025-03-14T04:19:07.4347859Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2025-03-14T04:19:07.4348081Z GITHUB_RUN_NUMBER=122697
2025-03-14T04:19:07.4348298Z TEST_CONFIG=dynamic_cpu_inductor_torchbench
2025-03-14T04:19:07.4348541Z GITHUB_REPOSITORY_OWNER_ID=21003710
2025-03-14T04:19:07.4348803Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2025-03-14T04:19:07.4349022Z IS_A100_RUNNER=0
2025-03-14T04:19:07.4349499Z SCRIBE_GRAPHQL_ACCESS_TOKEN=***
2025-03-14T04:19:07.4349720Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot
2025-03-14T04:19:07.4349952Z GITHUB_REF_TYPE=branch
2025-03-14T04:19:07.4350145Z TORCH_CUDA_ARCH_LIST=Maxwell
2025-03-14T04:19:07.4350371Z BASE_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:19:07.4350605Z XLA_CUDA=
2025-03-14T04:19:07.4350872Z HUGGING_FACE_HUB_TOKEN=***
2025-03-14T04:19:07.4351290Z ***
2025-03-14T04:19:07.4351466Z GITHUB_REPOSITORY_ID=65600975
2025-03-14T04:19:07.4351673Z GITHUB_ACTIONS=true
2025-03-14T04:19:07.4351878Z SHA1=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:19:07.4352131Z GITHUB_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:19:07.4352478Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/inductor.yml@refs/heads/main
2025-03-14T04:19:07.4352793Z UCC_HOME=/usr
2025-03-14T04:19:07.4352974Z TORCH_SERIALIZATION_DEBUG=1
2025-03-14T04:19:07.4353183Z VERBOSE_TEST_LOGS=False
2025-03-14T04:19:07.4353376Z GITHUB_REF=refs/heads/main
2025-03-14T04:19:07.4353573Z SHARD_NUMBER=1
2025-03-14T04:19:07.4353748Z GITHUB_REF_PROTECTED=true
2025-03-14T04:19:07.4353942Z HOME=/var/lib/jenkins
2025-03-14T04:19:07.4354159Z GITHUB_API_URL=https://api.github.com
2025-03-14T04:19:07.4354392Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2025-03-14T04:19:07.4354605Z UCX_COMMIT=
2025-03-14T04:19:07.4354767Z NUM_TEST_SHARDS=2
2025-03-14T04:19:07.4354940Z UCX_HOME=/usr
2025-03-14T04:19:07.4355303Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_cf747ae1-d501-4db5-a2cf-d8c5918f871b
2025-03-14T04:19:07.4355871Z JOB_NAME=linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:19:07.4356430Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_cf747ae1-d501-4db5-a2cf-d8c5918f871b
2025-03-14T04:19:07.4357092Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2025-03-14T04:19:07.4357425Z GITHUB_EVENT_NAME=push
2025-03-14T04:19:07.4357605Z DASHBOARD_TAG=
2025-03-14T04:19:07.4357781Z GITHUB_RUN_ID=13849515380
2025-03-14T04:19:07.4358179Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_cf747ae1-d501-4db5-a2cf-d8c5918f871b
2025-03-14T04:19:07.4358662Z GITHUB_ACTOR=pytorchmergebot
2025-03-14T04:19:07.4358860Z PR_NUMBER=
2025-03-14T04:19:07.4359020Z DESIRED_CUDA=
2025-03-14T04:19:07.4359188Z GITHUB_RUN_ATTEMPT=1
2025-03-14T04:19:07.4359369Z VALGRIND=ON
2025-03-14T04:19:07.4359538Z ANACONDA_PYTHON_VERSION=3.9
2025-03-14T04:19:07.4359773Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2025-03-14T04:19:07.4360014Z TERM=vt100
2025-03-14T04:19:07.4360177Z INSTALLED_VISION=yes
2025-03-14T04:19:07.4360353Z BRANCH=main
2025-03-14T04:19:07.4360517Z SCCACHE_REGION=us-east-1
2025-03-14T04:19:07.4360928Z OPENSSL_ROOT_DIR=/opt/openssl
2025-03-14T04:19:07.4361141Z CUDA_PATH=/usr/local/cuda
2025-03-14T04:19:07.4361503Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2025-03-14T04:19:07.4361903Z GITHUB_SERVER_URL=https://github.com
2025-03-14T04:19:07.4362127Z UCC_COMMIT=
2025-03-14T04:19:07.4362291Z REENABLED_ISSUES=
2025-03-14T04:19:07.4362465Z DOCS=yes
2025-03-14T04:19:07.4362623Z SHLVL=1
2025-03-14T04:19:07.4362778Z MAX_JOBS=30
2025-03-14T04:19:07.4362943Z GITHUB_ACTOR_ID=97764156
2025-03-14T04:19:07.4363194Z GITHUB_WORKFLOW_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:19:07.4363452Z GITHUB_REF_NAME=main
2025-03-14T04:19:07.4363716Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2025-03-14T04:19:07.4364005Z GITHUB_JOB=test
2025-03-14T04:19:07.4364180Z NO_TEST_TIMEOUT=False
2025-03-14T04:19:07.4364366Z TD_DISTRIBUTED=False
2025-03-14T04:19:07.4364551Z GITHUB_REPOSITORY=pytorch/pytorch
2025-03-14T04:19:07.4364768Z GITHUB_RETENTION_DAYS=90
2025-03-14T04:19:07.4364958Z OPENSSL_DIR=/opt/openssl
2025-03-14T04:19:07.4365157Z GITHUB_ACTION_REPOSITORY=
2025-03-14T04:19:07.4365636Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-14T04:19:07.4366125Z GITHUB_BASE_REF=
2025-03-14T04:19:07.4366291Z INSTALLED_ACL=
2025-03-14T04:19:07.4366586Z ARTIFACTS_FILE_SUFFIX=test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754842362
2025-03-14T04:19:07.4366909Z CI=true
2025-03-14T04:19:07.4367073Z GITHUB_REPOSITORY_OWNER=pytorch
2025-03-14T04:19:07.4367273Z JOB_ID=38754842362
2025-03-14T04:19:07.4367443Z INSTALLED_PROTOBUF=yes
2025-03-14T04:19:07.4367624Z GITHUB_HEAD_REF=
2025-03-14T04:19:07.4367795Z GITHUB_ACTION_REF=
2025-03-14T04:19:07.4368000Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2025-03-14T04:19:07.4368239Z TEST_SHOWLOCALS=False
2025-03-14T04:19:07.4368413Z GITHUB_WORKFLOW=inductor
2025-03-14T04:19:07.4368607Z DEBIAN_FRONTEND=noninteractive
2025-03-14T04:19:07.4368998Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_cf747ae1-d501-4db5-a2cf-d8c5918f871b
2025-03-14T04:19:07.4369382Z NO_TD=False
2025-03-14T04:19:07.4369551Z SKIP_SCCACHE_INITIALIZATION=1
2025-03-14T04:19:07.4369745Z _=/usr/bin/env
2025-03-14T04:19:07.4369910Z + echo 'Testing pytorch'
2025-03-14T04:19:07.4370088Z Testing pytorch
2025-03-14T04:19:07.4370286Z + export LANG=C.UTF-8
2025-03-14T04:19:07.4370460Z + LANG=C.UTF-8
2025-03-14T04:19:07.4370619Z + PR_NUMBER=
2025-03-14T04:19:07.4370820Z + [[ dynamic_cpu_inductor_torchbench == \d\e\f\a\u\l\t ]]
2025-03-14T04:19:07.4371118Z + [[ dynamic_cpu_inductor_torchbench == \d\i\s\t\r\i\b\u\t\e\d ]]
2025-03-14T04:19:07.4371405Z + [[ dynamic_cpu_inductor_torchbench == \s\l\o\w ]]
2025-03-14T04:19:07.4371788Z + [[ linux-jammy-py3.9-gcc11-build == *slow-gradcheck* ]]
2025-03-14T04:19:07.4372072Z + [[ linux-jammy-py3.9-gcc11-build == *cuda* ]]
2025-03-14T04:19:07.4372337Z + [[ linux-jammy-py3.9-gcc11-build == *rocm* ]]
2025-03-14T04:19:07.4372730Z + [[ linux-jammy-py3.9-gcc11-build == *xpu* ]]
2025-03-14T04:19:07.4373012Z + [[ dynamic_cpu_inductor_torchbench == *crossref* ]]
2025-03-14T04:19:07.4373270Z + [[ linux-jammy-py3.9-gcc11-build == *rocm* ]]
2025-03-14T04:19:07.4373609Z + [[ linux-jammy-py3.9-gcc11-build == *xpu* ]]
2025-03-14T04:19:07.4373863Z + [[ linux-jammy-py3.9-gcc11-build != *-bazel-* ]]
2025-03-14T04:19:07.4374110Z + pip_install --user ninja==1.10.2
2025-03-14T04:19:07.4374380Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-14T04:19:07.4374698Z + python3 -m pip install --progress-bar off --user ninja==1.10.2
2025-03-14T04:19:07.7946233Z Collecting ninja==1.10.2
2025-03-14T04:19:07.8052520Z   Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB)
2025-03-14T04:19:07.8198888Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB)
2025-03-14T04:19:08.3093664Z Installing collected packages: ninja
2025-03-14T04:19:08.3168696Z   WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH.
2025-03-14T04:19:08.3169353Z   Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
2025-03-14T04:19:08.3233596Z Successfully installed ninja-1.10.2
2025-03-14T04:19:08.3986225Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-14T04:19:08.3987351Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-14T04:19:08.3987973Z + [[ linux-jammy-py3.9-gcc11-build == *aarch64* ]]
2025-03-14T04:19:08.3988232Z + install_tlparse
2025-03-14T04:19:08.3988428Z + pip_install --user tlparse==0.3.30
2025-03-14T04:19:08.3988736Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-14T04:19:08.3989048Z + python3 -m pip install --progress-bar off --user tlparse==0.3.30
2025-03-14T04:19:08.6918867Z Collecting tlparse==0.3.30
2025-03-14T04:19:08.7000635Z   Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.9 kB)
2025-03-14T04:19:08.7185216Z Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB)
2025-03-14T04:19:09.2228307Z Installing collected packages: tlparse
2025-03-14T04:19:09.2567510Z Successfully installed tlparse-0.3.30
2025-03-14T04:19:09.3372711Z ++ python -m site --user-base
2025-03-14T04:19:09.3610972Z + PATH=/var/lib/jenkins/.local/bin:/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-14T04:19:09.3611827Z + [[ linux-jammy-py3.9-gcc11-build == *asan* ]]
2025-03-14T04:19:09.3612123Z + [[ linux-jammy-py3.9-gcc11-build == *-debug* ]]
2025-03-14T04:19:09.3612472Z + [[ linux-jammy-py3.9-gcc11-build != *-bazel-* ]]
2025-03-14T04:19:09.3612877Z + echo 'We are not in debug mode: linux-jammy-py3.9-gcc11-build. Expect the assertion to pass'
2025-03-14T04:19:09.3613303Z We are not in debug mode: linux-jammy-py3.9-gcc11-build. Expect the assertion to pass
2025-03-14T04:19:09.3613595Z + cd test
2025-03-14T04:19:09.3613839Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)'
2025-03-14T04:19:10.4826452Z + [[ dynamic_cpu_inductor_torchbench == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]]
2025-03-14T04:19:10.4826845Z + [[ dynamic_cpu_inductor_torchbench == \n\o\g\p\u\_\A\V\X\5\1\2 ]]
2025-03-14T04:19:10.4853005Z + DYNAMO_BENCHMARK_FLAGS=()
2025-03-14T04:19:10.4853382Z + [[ dynamic_cpu_inductor_torchbench == *pr_time_benchmarks* ]]
2025-03-14T04:19:10.4853700Z + [[ dynamic_cpu_inductor_torchbench == *dynamo_eager* ]]
2025-03-14T04:19:10.4853977Z + [[ dynamic_cpu_inductor_torchbench == *aot_eager* ]]
2025-03-14T04:19:10.4855742Z + [[ dynamic_cpu_inductor_torchbench == *aot_inductor* ]]
2025-03-14T04:19:10.4856070Z + [[ dynamic_cpu_inductor_torchbench == *max_autotune_inductor* ]]
2025-03-14T04:19:10.4856368Z + [[ dynamic_cpu_inductor_torchbench == *inductor* ]]
2025-03-14T04:19:10.4856775Z + [[ dynamic_cpu_inductor_torchbench != *perf* ]]
2025-03-14T04:19:10.4857053Z + DYNAMO_BENCHMARK_FLAGS+=(--inductor)
2025-03-14T04:19:10.4857305Z + [[ dynamic_cpu_inductor_torchbench == *dynamic* ]]
2025-03-14T04:19:10.4857631Z + DYNAMO_BENCHMARK_FLAGS+=(--dynamic-shapes --dynamic-batch-only)
2025-03-14T04:19:10.4857930Z + [[ dynamic_cpu_inductor_torchbench == *cpu* ]]
2025-03-14T04:19:10.4858174Z + DYNAMO_BENCHMARK_FLAGS+=(--device cpu)
2025-03-14T04:19:10.4858431Z + [[ linux-jammy-py3.9-gcc11-build == *libtorch* ]]
2025-03-14T04:19:10.4858712Z + [[ linux-jammy-py3.9-gcc11-build == *-bazel-* ]]
2025-03-14T04:19:10.4858951Z + cd test
2025-03-14T04:19:10.4859163Z + python -c 'import torch; print(torch.__config__.show())'
2025-03-14T04:19:11.4158995Z PyTorch built with:
2025-03-14T04:19:11.4159359Z   - GCC 11.4
2025-03-14T04:19:11.4159541Z   - C++ Version: 201703
2025-03-14T04:19:11.4159964Z   - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
2025-03-14T04:19:11.4160432Z   - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d)
2025-03-14T04:19:11.4160998Z   - OpenMP 201511 (a.k.a. OpenMP 4.5)
2025-03-14T04:19:11.4161254Z   - LAPACK is enabled (usually provided by MKL)
2025-03-14T04:19:11.4161480Z   - NNPACK is enabled
2025-03-14T04:19:11.4161692Z   - CPU capability usage: AVX512
2025-03-14T04:19:11.4164450Z   - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4, CXX_COMPILER=/opt/cache/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=1 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Werror -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.8.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 
2025-03-14T04:19:11.4181868Z 
2025-03-14T04:19:11.6219286Z + cd test
2025-03-14T04:19:11.6219698Z + python -c 'import torch; print(torch.__config__.parallel_info())'
2025-03-14T04:19:12.5321340Z ATen/Parallel:
2025-03-14T04:19:12.5323311Z 	at::get_num_threads() : 16
2025-03-14T04:19:12.5324531Z 	at::get_num_interop_threads() : 16
2025-03-14T04:19:12.5325698Z OpenMP 201511 (a.k.a. OpenMP 4.5)
2025-03-14T04:19:12.5328151Z 	omp_get_max_threads() : 16
2025-03-14T04:19:12.5328642Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
2025-03-14T04:19:12.5329110Z 	mkl_get_max_threads() : 16
2025-03-14T04:19:12.5329392Z Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d)
2025-03-14T04:19:12.5329714Z std::thread::hardware_concurrency() : 32
2025-03-14T04:19:12.5329946Z Environment variables:
2025-03-14T04:19:12.5330145Z 	OMP_NUM_THREADS : [not set]
2025-03-14T04:19:12.5330350Z 	MKL_NUM_THREADS : [not set]
2025-03-14T04:19:12.5333071Z ATen parallel backend: OpenMP
2025-03-14T04:19:12.5333209Z 
2025-03-14T04:19:12.7290406Z + [[ dynamic_cpu_inductor_torchbench == *numpy_2* ]]
2025-03-14T04:19:12.7290771Z + [[ linux-jammy-py3.9-gcc11-build == *aarch64* ]]
2025-03-14T04:19:12.7291466Z + [[ dynamic_cpu_inductor_torchbench == *backward* ]]
2025-03-14T04:19:12.7291882Z + [[ dynamic_cpu_inductor_torchbench == *xla* ]]
2025-03-14T04:19:12.7292178Z + [[ dynamic_cpu_inductor_torchbench == *executorch* ]]
2025-03-14T04:19:12.7292621Z + [[ dynamic_cpu_inductor_torchbench == \j\i\t\_\l\e\g\a\c\y ]]
2025-03-14T04:19:12.7292910Z + [[ linux-jammy-py3.9-gcc11-build == *libtorch* ]]
2025-03-14T04:19:12.7293182Z + [[ dynamic_cpu_inductor_torchbench == distributed ]]
2025-03-14T04:19:12.7293488Z + [[ dynamic_cpu_inductor_torchbench == *inductor_distributed* ]]
2025-03-14T04:19:12.7293792Z + [[ dynamic_cpu_inductor_torchbench == *inductor-halide* ]]
2025-03-14T04:19:12.7294093Z + [[ dynamic_cpu_inductor_torchbench == *inductor-triton-cpu* ]]
2025-03-14T04:19:12.7294413Z + [[ dynamic_cpu_inductor_torchbench == *inductor-micro-benchmark* ]]
2025-03-14T04:19:12.7294716Z + [[ dynamic_cpu_inductor_torchbench == *huggingface* ]]
2025-03-14T04:19:12.7294972Z + [[ dynamic_cpu_inductor_torchbench == *timm* ]]
2025-03-14T04:19:12.7295226Z + [[ dynamic_cpu_inductor_torchbench == cachebench ]]
2025-03-14T04:19:12.7295500Z + [[ dynamic_cpu_inductor_torchbench == verify_cachebench ]]
2025-03-14T04:19:12.7295770Z + [[ dynamic_cpu_inductor_torchbench == *torchbench* ]]
2025-03-14T04:19:12.7296032Z + [[ dynamic_cpu_inductor_torchbench == *cpu* ]]
2025-03-14T04:19:12.7296266Z + install_torchaudio cpu
2025-03-14T04:19:12.7296459Z + local commit
2025-03-14T04:19:12.7296640Z ++ get_pinned_commit audio
2025-03-14T04:19:12.7296847Z ++ cat .github/ci_commit_pins/audio.txt
2025-03-14T04:19:12.7308446Z + commit=c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-14T04:19:12.7308783Z + [[ cpu == \c\u\d\a ]]
2025-03-14T04:19:12.7309213Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-14T04:19:12.7309676Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-14T04:19:12.7310219Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-14T04:19:12.9951943Z Collecting git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-14T04:19:12.9953357Z   Cloning https://github.com/pytorch/audio.git (to revision c670ad81fda266b6598aeeef434583eb98197ae8) to /tmp/pip-req-build-wwu140h_
2025-03-14T04:19:12.9984168Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/audio.git /tmp/pip-req-build-wwu140h_
2025-03-14T04:19:13.6730789Z   Running command git rev-parse -q --verify 'sha^c670ad81fda266b6598aeeef434583eb98197ae8'
2025-03-14T04:19:13.6760888Z   Running command git fetch -q https://github.com/pytorch/audio.git c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-14T04:19:13.7560989Z   Resolved https://github.com/pytorch/audio.git to commit c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-14T04:19:13.7561595Z   Running command git submodule update --init --recursive -q
2025-03-14T04:19:15.4613664Z   Preparing metadata (setup.py) ... [?25l- done
2025-03-14T04:19:15.4648458Z [?25hRequirement already satisfied: torch in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchaudio==2.6.0a0+c670ad8) (2.8.0a0+gitaed0b7a)
2025-03-14T04:19:15.4672980Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (3.16.1)
2025-03-14T04:19:15.4673756Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (4.12.2)
2025-03-14T04:19:15.4678821Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (1.13.3)
2025-03-14T04:19:15.4679559Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (2.8.8)
2025-03-14T04:19:15.4680539Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (3.1.6)
2025-03-14T04:19:15.4681182Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (2024.10.0)
2025-03-14T04:19:15.4697545Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from sympy>=1.13.3->torch->torchaudio==2.6.0a0+c670ad8) (1.3.0)
2025-03-14T04:19:15.5101757Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from jinja2->torch->torchaudio==2.6.0a0+c670ad8) (3.0.2)
2025-03-14T04:19:15.5149092Z Building wheels for collected packages: torchaudio
2025-03-14T04:20:06.8253108Z   Building wheel for torchaudio (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done
2025-03-14T04:20:06.8283503Z [?25h  Created wheel for torchaudio: filename=torchaudio-2.6.0a0+c670ad8-cp39-cp39-linux_x86_64.whl size=1820578 sha256=c8cddfc5d708fe19671471d39a802f3f77314a268f5ddc6ee56c5072c9f191cc
2025-03-14T04:20:06.8284373Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/1c/6f/84/4c8de1f050144e10889ee8fe7b3a86ac99233001c78656be9d
2025-03-14T04:20:06.8319275Z Successfully built torchaudio
2025-03-14T04:20:07.2718486Z Installing collected packages: torchaudio
2025-03-14T04:20:07.4283454Z Successfully installed torchaudio-2.6.0a0+c670ad8
2025-03-14T04:20:07.5453560Z + install_torchvision
2025-03-14T04:20:07.5457427Z + local orig_preload
2025-03-14T04:20:07.5459768Z + local commit
2025-03-14T04:20:07.5460822Z ++ get_pinned_commit vision
2025-03-14T04:20:07.5461130Z ++ cat .github/ci_commit_pins/vision.txt
2025-03-14T04:20:07.5470456Z + commit=d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:20:07.5470816Z + orig_preload=
2025-03-14T04:20:07.5471005Z + '[' -n '' ']'
2025-03-14T04:20:07.5471396Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:20:07.5471860Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-14T04:20:07.5472345Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:20:07.8132642Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:20:07.8134026Z   Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-ajt6yyze
2025-03-14T04:20:07.8165148Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-ajt6yyze
2025-03-14T04:20:09.2676892Z   Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f'
2025-03-14T04:20:09.2703465Z   Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:20:09.3576991Z   Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:20:09.6745669Z   Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:20:11.4780920Z   Preparing metadata (setup.py) ... [?25l- \ done
2025-03-14T04:20:11.4818165Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchvision==0.19.0a0+d23a6e1) (1.22.4)
2025-03-14T04:20:11.4823482Z Requirement already satisfied: torch in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.8.0a0+gitaed0b7a)
2025-03-14T04:20:11.4824209Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchvision==0.19.0a0+d23a6e1) (11.0.0)
2025-03-14T04:20:11.4886239Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.16.1)
2025-03-14T04:20:11.4887034Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2)
2025-03-14T04:20:11.4887876Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.13.3)
2025-03-14T04:20:11.4888535Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.8.8)
2025-03-14T04:20:11.4890448Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.6)
2025-03-14T04:20:11.4897518Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.10.0)
2025-03-14T04:20:11.4913820Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from sympy>=1.13.3->torch->torchvision==0.19.0a0+d23a6e1) (1.3.0)
2025-03-14T04:20:11.5330226Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from jinja2->torch->torchvision==0.19.0a0+d23a6e1) (3.0.2)
2025-03-14T04:20:11.5407362Z Building wheels for collected packages: torchvision
2025-03-14T04:20:33.4513531Z   Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / done
2025-03-14T04:20:33.4538990Z [?25h  Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp39-cp39-linux_x86_64.whl size=1218778 sha256=bfdeeafebb538d16122d5711a87c562eccb7710f69fbe89354029649a48f28de
2025-03-14T04:20:33.4539803Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/76/25/46/00629fe1ec5f276eb28faecc4ae7c48c9ef9e6bbaa0691ad87
2025-03-14T04:20:33.4577004Z Successfully built torchvision
2025-03-14T04:20:33.8953393Z Installing collected packages: torchvision
2025-03-14T04:20:34.1963062Z Successfully installed torchvision-0.19.0a0+d23a6e1
2025-03-14T04:20:34.2939765Z + '[' -n '' ']'
2025-03-14T04:20:34.2946055Z + TORCH_CUDA_ARCH_LIST='8.0;8.6'
2025-03-14T04:20:34.2947965Z + pip_install git+https://github.com/pytorch/ao.git
2025-03-14T04:20:34.2948380Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-14T04:20:34.2948764Z + python3 -m pip install --progress-bar off git+https://github.com/pytorch/ao.git
2025-03-14T04:20:34.5573330Z Collecting git+https://github.com/pytorch/ao.git
2025-03-14T04:20:34.5573768Z   Cloning https://github.com/pytorch/ao.git to /tmp/pip-req-build-toprblbf
2025-03-14T04:20:34.5610530Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/ao.git /tmp/pip-req-build-toprblbf
2025-03-14T04:20:35.3799194Z   Resolved https://github.com/pytorch/ao.git to commit 9259584f98db0760b27492a63050a2915c753dbe
2025-03-14T04:20:35.3799642Z   Running command git submodule update --init --recursive -q
2025-03-14T04:20:39.0869849Z   Preparing metadata (setup.py) ... [?25l- done
2025-03-14T04:20:39.0915518Z [?25hBuilding wheels for collected packages: torchao
2025-03-14T04:20:41.0092172Z   Building wheel for torchao (setup.py) ... [?25l- \ | done
2025-03-14T04:20:41.0105154Z [?25h  Created wheel for torchao: filename=torchao-0.10.0+git9259584f-py3-none-any.whl size=687512 sha256=0c7a3ef5346c37aba572d3f580c02e0f3052789f9b5a6501ca13c8b72f80f2b9
2025-03-14T04:20:41.0111463Z   Stored in directory: /tmp/pip-ephem-wheel-cache-x4yn79p_/wheels/7d/5c/37/b607f0d104c0d0de07b506bc734c280ece23dce90e0c5cddc7
2025-03-14T04:20:41.0136414Z Successfully built torchao
2025-03-14T04:20:41.4905861Z Installing collected packages: torchao
2025-03-14T04:20:41.9235137Z Successfully installed torchao-0.10.0+git9259584f
2025-03-14T04:20:42.1108323Z + id=0
2025-03-14T04:20:42.1109043Z + pip_install opencv-python==4.8.0.74
2025-03-14T04:20:42.1109376Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-14T04:20:42.1109715Z + python3 -m pip install --progress-bar off opencv-python==4.8.0.74
2025-03-14T04:20:42.4590411Z Collecting opencv-python==4.8.0.74
2025-03-14T04:20:42.4718489Z   Downloading opencv_python-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB)
2025-03-14T04:20:42.4824140Z Requirement already satisfied: numpy>=1.17.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from opencv-python==4.8.0.74) (1.22.4)
2025-03-14T04:20:42.4903597Z Downloading opencv_python-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.7 MB)
2025-03-14T04:20:43.3978890Z Installing collected packages: opencv-python
2025-03-14T04:20:44.1762810Z Successfully installed opencv-python-4.8.0.74
2025-03-14T04:20:44.2642902Z + [[ dynamic_cpu_inductor_torchbench == *inductor_torchbench_smoketest_perf* ]]
2025-03-14T04:20:44.2643638Z + [[ dynamic_cpu_inductor_torchbench == *inductor_torchbench_cpu_smoketest_perf* ]]
2025-03-14T04:20:44.2644009Z + [[ dynamic_cpu_inductor_torchbench == *torchbench_gcp_smoketest* ]]
2025-03-14T04:20:44.2644299Z + checkout_install_torchbench
2025-03-14T04:20:44.2644497Z + local commit
2025-03-14T04:20:44.2644673Z ++ get_pinned_commit torchbench
2025-03-14T04:20:44.2644892Z ++ cat .github/ci_commit_pins/torchbench.txt
2025-03-14T04:20:44.2652970Z + commit=373ffb19dc470f4423a3176a4133f8f4b3cdb5bd
2025-03-14T04:20:44.2653528Z + git clone https://github.com/pytorch/benchmark torchbench
2025-03-14T04:20:44.2669708Z Cloning into 'torchbench'...
2025-03-14T04:20:44.5195077Z remote: Enumerating objects: 35366, done.
2025-03-14T04:20:44.5195448Z remote: Counting objects:   0% (1/5443)
2025-03-14T04:20:44.5195784Z remote: Counting objects:   1% (55/5443)
2025-03-14T04:20:44.5196064Z remote: Counting objects:   2% (109/5443)
2025-03-14T04:20:44.5196351Z remote: Counting objects:   3% (164/5443)
2025-03-14T04:20:44.5196655Z remote: Counting objects:   4% (218/5443)
2025-03-14T04:20:44.5196928Z remote: Counting objects:   5% (273/5443)
2025-03-14T04:20:44.5197190Z remote: Counting objects:   6% (327/5443)
2025-03-14T04:20:44.5197463Z remote: Counting objects:   7% (382/5443)
2025-03-14T04:20:44.5197790Z remote: Counting objects:   8% (436/5443)
2025-03-14T04:20:44.5198066Z remote: Counting objects:   9% (490/5443)
2025-03-14T04:20:44.5198322Z remote: Counting objects:  10% (545/5443)
2025-03-14T04:20:44.5198583Z remote: Counting objects:  11% (599/5443)
2025-03-14T04:20:44.5198846Z remote: Counting objects:  12% (654/5443)
2025-03-14T04:20:44.5199109Z remote: Counting objects:  13% (708/5443)
2025-03-14T04:20:44.5199385Z remote: Counting objects:  14% (763/5443)
2025-03-14T04:20:44.5199649Z remote: Counting objects:  15% (817/5443)
2025-03-14T04:20:44.5199925Z remote: Counting objects:  16% (871/5443)
2025-03-14T04:20:44.5200197Z remote: Counting objects:  17% (926/5443)
2025-03-14T04:20:44.5200464Z remote: Counting objects:  18% (980/5443)
2025-03-14T04:20:44.5200738Z remote: Counting objects:  19% (1035/5443)
2025-03-14T04:20:44.5201021Z remote: Counting objects:  20% (1089/5443)
2025-03-14T04:20:44.5201294Z remote: Counting objects:  21% (1144/5443)
2025-03-14T04:20:44.5201549Z remote: Counting objects:  22% (1198/5443)
2025-03-14T04:20:44.5201814Z remote: Counting objects:  23% (1252/5443)
2025-03-14T04:20:44.5202079Z remote: Counting objects:  24% (1307/5443)
2025-03-14T04:20:44.5202406Z remote: Counting objects:  25% (1361/5443)
2025-03-14T04:20:44.5202687Z remote: Counting objects:  26% (1416/5443)
2025-03-14T04:20:44.5205211Z remote: Counting objects:  27% (1470/5443)
2025-03-14T04:20:44.5205546Z remote: Counting objects:  28% (1525/5443)
2025-03-14T04:20:44.5205826Z remote: Counting objects:  29% (1579/5443)
2025-03-14T04:20:44.5206100Z remote: Counting objects:  30% (1633/5443)
2025-03-14T04:20:44.5206782Z remote: Counting objects:  31% (1688/5443)
2025-03-14T04:20:44.5207064Z remote: Counting objects:  32% (1742/5443)
2025-03-14T04:20:44.5207332Z remote: Counting objects:  33% (1797/5443)
2025-03-14T04:20:44.5207737Z remote: Counting objects:  34% (1851/5443)
2025-03-14T04:20:44.5208022Z remote: Counting objects:  35% (1906/5443)
2025-03-14T04:20:44.5208309Z remote: Counting objects:  36% (1960/5443)
2025-03-14T04:20:44.5208594Z remote: Counting objects:  37% (2014/5443)
2025-03-14T04:20:44.5208889Z remote: Counting objects:  38% (2069/5443)
2025-03-14T04:20:44.5209173Z remote: Counting objects:  39% (2123/5443)
2025-03-14T04:20:44.5209457Z remote: Counting objects:  40% (2178/5443)
2025-03-14T04:20:44.5209736Z remote: Counting objects:  41% (2232/5443)
2025-03-14T04:20:44.5210019Z remote: Counting objects:  42% (2287/5443)
2025-03-14T04:20:44.5210298Z remote: Counting objects:  43% (2341/5443)
2025-03-14T04:20:44.5210579Z remote: Counting objects:  44% (2395/5443)
2025-03-14T04:20:44.5210864Z remote: Counting objects:  45% (2450/5443)
2025-03-14T04:20:44.5211158Z remote: Counting objects:  46% (2504/5443)
2025-03-14T04:20:44.5211568Z remote: Counting objects:  47% (2559/5443)
2025-03-14T04:20:44.5211964Z remote: Counting objects:  48% (2613/5443)
2025-03-14T04:20:44.5212272Z remote: Counting objects:  49% (2668/5443)
2025-03-14T04:20:44.5212604Z remote: Counting objects:  50% (2722/5443)
2025-03-14T04:20:44.5212892Z remote: Counting objects:  51% (2776/5443)
2025-03-14T04:20:44.5213180Z remote: Counting objects:  52% (2831/5443)
2025-03-14T04:20:44.5213466Z remote: Counting objects:  53% (2885/5443)
2025-03-14T04:20:44.5213751Z remote: Counting objects:  54% (2940/5443)
2025-03-14T04:20:44.5214038Z remote: Counting objects:  55% (2994/5443)
2025-03-14T04:20:44.5214311Z remote: Counting objects:  56% (3049/5443)
2025-03-14T04:20:44.5214580Z remote: Counting objects:  57% (3103/5443)
2025-03-14T04:20:44.5214854Z remote: Counting objects:  58% (3157/5443)
2025-03-14T04:20:44.5215132Z remote: Counting objects:  59% (3212/5443)
2025-03-14T04:20:44.5215417Z remote: Counting objects:  60% (3266/5443)
2025-03-14T04:20:44.5215699Z remote: Counting objects:  61% (3321/5443)
2025-03-14T04:20:44.5215985Z remote: Counting objects:  62% (3375/5443)
2025-03-14T04:20:44.5216328Z remote: Counting objects:  63% (3430/5443)
2025-03-14T04:20:44.5216615Z remote: Counting objects:  64% (3484/5443)
2025-03-14T04:20:44.5216895Z remote: Counting objects:  65% (3538/5443)
2025-03-14T04:20:44.5217169Z remote: Counting objects:  66% (3593/5443)
2025-03-14T04:20:44.5217450Z remote: Counting objects:  67% (3647/5443)
2025-03-14T04:20:44.5217730Z remote: Counting objects:  68% (3702/5443)
2025-03-14T04:20:44.5218010Z remote: Counting objects:  69% (3756/5443)
2025-03-14T04:20:44.5218291Z remote: Counting objects:  70% (3811/5443)
2025-03-14T04:20:44.5218571Z remote: Counting objects:  71% (3865/5443)
2025-03-14T04:20:44.5218857Z remote: Counting objects:  72% (3919/5443)
2025-03-14T04:20:44.5219139Z remote: Counting objects:  73% (3974/5443)
2025-03-14T04:20:44.5219418Z remote: Counting objects:  74% (4028/5443)
2025-03-14T04:20:44.5219705Z remote: Counting objects:  75% (4083/5443)
2025-03-14T04:20:44.5219986Z remote: Counting objects:  76% (4137/5443)
2025-03-14T04:20:44.5220265Z remote: Counting objects:  77% (4192/5443)
2025-03-14T04:20:44.5220548Z remote: Counting objects:  78% (4246/5443)
2025-03-14T04:20:44.5220829Z remote: Counting objects:  79% (4300/5443)
2025-03-14T04:20:44.5221113Z remote: Counting objects:  80% (4355/5443)
2025-03-14T04:20:44.5221394Z remote: Counting objects:  81% (4409/5443)
2025-03-14T04:20:44.5221675Z remote: Counting objects:  82% (4464/5443)
2025-03-14T04:20:44.5221958Z remote: Counting objects:  83% (4518/5443)
2025-03-14T04:20:44.5222247Z remote: Counting objects:  84% (4573/5443)
2025-03-14T04:20:44.5222523Z remote: Counting objects:  85% (4627/5443)
2025-03-14T04:20:44.5222866Z remote: Counting objects:  86% (4681/5443)
2025-03-14T04:20:44.5223155Z remote: Counting objects:  87% (4736/5443)
2025-03-14T04:20:44.5223421Z remote: Counting objects:  88% (4790/5443)
2025-03-14T04:20:44.5223743Z remote: Counting objects:  89% (4845/5443)
2025-03-14T04:20:44.5224013Z remote: Counting objects:  90% (4899/5443)
2025-03-14T04:20:44.5224279Z remote: Counting objects:  91% (4954/5443)
2025-03-14T04:20:44.5224545Z remote: Counting objects:  92% (5008/5443)
2025-03-14T04:20:44.5224813Z remote: Counting objects:  93% (5062/5443)
2025-03-14T04:20:44.5225082Z remote: Counting objects:  94% (5117/5443)
2025-03-14T04:20:44.5225350Z remote: Counting objects:  95% (5171/5443)
2025-03-14T04:20:44.5225621Z remote: Counting objects:  96% (5226/5443)
2025-03-14T04:20:44.5225891Z remote: Counting objects:  97% (5280/5443)
2025-03-14T04:20:44.5226157Z remote: Counting objects:  98% (5335/5443)
2025-03-14T04:20:44.5226429Z remote: Counting objects:  99% (5389/5443)
2025-03-14T04:20:44.5226702Z remote: Counting objects: 100% (5443/5443)
2025-03-14T04:20:44.5226997Z remote: Counting objects: 100% (5443/5443), done.
2025-03-14T04:20:44.5227645Z remote: Compressing objects:   0% (1/605)
2025-03-14T04:20:44.5272704Z remote: Compressing objects:   1% (7/605)
2025-03-14T04:20:44.5289857Z remote: Compressing objects:   2% (13/605)
2025-03-14T04:20:44.5315555Z remote: Compressing objects:   3% (19/605)
2025-03-14T04:20:44.5330121Z remote: Compressing objects:   4% (25/605)
2025-03-14T04:20:44.5356122Z remote: Compressing objects:   5% (31/605)
2025-03-14T04:20:44.5377719Z remote: Compressing objects:   6% (37/605)
2025-03-14T04:20:44.5393062Z remote: Compressing objects:   7% (43/605)
2025-03-14T04:20:44.5405452Z remote: Compressing objects:   8% (49/605)
2025-03-14T04:20:44.5407537Z remote: Compressing objects:   9% (55/605)
2025-03-14T04:20:44.5409090Z remote: Compressing objects:  10% (61/605)
2025-03-14T04:20:44.5526128Z remote: Compressing objects:  11% (67/605)
2025-03-14T04:20:44.5626760Z remote: Compressing objects:  12% (73/605)
2025-03-14T04:20:44.5643473Z remote: Compressing objects:  13% (79/605)
2025-03-14T04:20:44.5764793Z remote: Compressing objects:  14% (85/605)
2025-03-14T04:20:44.5823597Z remote: Compressing objects:  15% (91/605)
2025-03-14T04:20:44.5849306Z remote: Compressing objects:  16% (97/605)
2025-03-14T04:20:44.5893256Z remote: Compressing objects:  17% (103/605)
2025-03-14T04:20:44.5938806Z remote: Compressing objects:  18% (109/605)
2025-03-14T04:20:44.5975106Z remote: Compressing objects:  19% (115/605)
2025-03-14T04:20:44.5993224Z remote: Compressing objects:  20% (121/605)
2025-03-14T04:20:44.6025536Z remote: Compressing objects:  21% (128/605)
2025-03-14T04:20:44.6053558Z remote: Compressing objects:  22% (134/605)
2025-03-14T04:20:44.6090227Z remote: Compressing objects:  23% (140/605)
2025-03-14T04:20:44.6111224Z remote: Compressing objects:  24% (146/605)
2025-03-14T04:20:44.6133428Z remote: Compressing objects:  25% (152/605)
2025-03-14T04:20:44.6151453Z remote: Compressing objects:  26% (158/605)
2025-03-14T04:20:44.6171222Z remote: Compressing objects:  27% (164/605)
2025-03-14T04:20:44.6186149Z remote: Compressing objects:  28% (170/605)
2025-03-14T04:20:44.6202941Z remote: Compressing objects:  29% (176/605)
2025-03-14T04:20:44.6217416Z remote: Compressing objects:  30% (182/605)
2025-03-14T04:20:44.6240026Z remote: Compressing objects:  31% (188/605)
2025-03-14T04:20:44.6250709Z remote: Compressing objects:  32% (194/605)
2025-03-14T04:20:44.6266151Z remote: Compressing objects:  33% (200/605)
2025-03-14T04:20:44.6281720Z remote: Compressing objects:  34% (206/605)
2025-03-14T04:20:44.6289524Z remote: Compressing objects:  35% (212/605)
2025-03-14T04:20:44.6297505Z remote: Compressing objects:  36% (218/605)
2025-03-14T04:20:44.6304793Z remote: Compressing objects:  37% (224/605)
2025-03-14T04:20:44.6313198Z remote: Compressing objects:  38% (230/605)
2025-03-14T04:20:44.6313567Z remote: Compressing objects:  39% (236/605)
2025-03-14T04:20:44.6320843Z remote: Compressing objects:  40% (242/605)
2025-03-14T04:20:44.6321452Z remote: Compressing objects:  41% (249/605)
2025-03-14T04:20:44.6321742Z remote: Compressing objects:  42% (255/605)
2025-03-14T04:20:44.6322024Z remote: Compressing objects:  43% (261/605)
2025-03-14T04:20:44.6326783Z remote: Compressing objects:  44% (267/605)
2025-03-14T04:20:44.6327126Z remote: Compressing objects:  45% (273/605)
2025-03-14T04:20:44.6327415Z remote: Compressing objects:  46% (279/605)
2025-03-14T04:20:44.6329946Z remote: Compressing objects:  47% (285/605)
2025-03-14T04:20:44.6343500Z remote: Compressing objects:  48% (291/605)
2025-03-14T04:20:44.6349212Z remote: Compressing objects:  49% (297/605)
2025-03-14T04:20:44.6349516Z remote: Compressing objects:  50% (303/605)
2025-03-14T04:20:44.6349809Z remote: Compressing objects:  51% (309/605)
2025-03-14T04:20:44.6350082Z remote: Compressing objects:  52% (315/605)
2025-03-14T04:20:44.6361704Z remote: Compressing objects:  53% (321/605)
2025-03-14T04:20:44.6362181Z remote: Compressing objects:  54% (327/605)
2025-03-14T04:20:44.6368305Z remote: Compressing objects:  55% (333/605)
2025-03-14T04:20:44.6368706Z remote: Compressing objects:  56% (339/605)
2025-03-14T04:20:44.6369014Z remote: Compressing objects:  57% (345/605)
2025-03-14T04:20:44.6369833Z remote: Compressing objects:  58% (351/605)
2025-03-14T04:20:44.6370152Z remote: Compressing objects:  59% (357/605)
2025-03-14T04:20:44.6370454Z remote: Compressing objects:  60% (363/605)
2025-03-14T04:20:44.6371103Z remote: Compressing objects:  61% (370/605)
2025-03-14T04:20:44.6371976Z remote: Compressing objects:  62% (376/605)
2025-03-14T04:20:44.6387137Z remote: Compressing objects:  63% (382/605)
2025-03-14T04:20:44.6388718Z remote: Compressing objects:  64% (388/605)
2025-03-14T04:20:44.6389211Z remote: Compressing objects:  65% (394/605)
2025-03-14T04:20:44.6395159Z remote: Compressing objects:  66% (400/605)
2025-03-14T04:20:44.6397126Z remote: Compressing objects:  67% (406/605)
2025-03-14T04:20:44.6397500Z remote: Compressing objects:  68% (412/605)
2025-03-14T04:20:44.6397914Z remote: Compressing objects:  69% (418/605)
2025-03-14T04:20:44.6403967Z remote: Compressing objects:  70% (424/605)
2025-03-14T04:20:44.6405621Z remote: Compressing objects:  71% (430/605)
2025-03-14T04:20:44.6405944Z remote: Compressing objects:  72% (436/605)
2025-03-14T04:20:44.6406244Z remote: Compressing objects:  73% (442/605)
2025-03-14T04:20:44.6406541Z remote: Compressing objects:  74% (448/605)
2025-03-14T04:20:44.6406830Z remote: Compressing objects:  75% (454/605)
2025-03-14T04:20:44.6408520Z remote: Compressing objects:  76% (460/605)
2025-03-14T04:20:44.6408831Z remote: Compressing objects:  77% (466/605)
2025-03-14T04:20:44.6409144Z remote: Compressing objects:  78% (472/605)
2025-03-14T04:20:44.6409431Z remote: Compressing objects:  79% (478/605)
2025-03-14T04:20:44.6409718Z remote: Compressing objects:  80% (484/605)
2025-03-14T04:20:44.6410010Z remote: Compressing objects:  81% (491/605)
2025-03-14T04:20:44.6410311Z remote: Compressing objects:  82% (497/605)
2025-03-14T04:20:44.6417976Z remote: Compressing objects:  83% (503/605)
2025-03-14T04:20:44.6419579Z remote: Compressing objects:  84% (509/605)
2025-03-14T04:20:44.6419891Z remote: Compressing objects:  85% (515/605)
2025-03-14T04:20:44.6420170Z remote: Compressing objects:  86% (521/605)
2025-03-14T04:20:44.6420446Z remote: Compressing objects:  87% (527/605)
2025-03-14T04:20:44.6420724Z remote: Compressing objects:  88% (533/605)
2025-03-14T04:20:44.6420999Z remote: Compressing objects:  89% (539/605)
2025-03-14T04:20:44.6421275Z remote: Compressing objects:  90% (545/605)
2025-03-14T04:20:44.6424937Z remote: Compressing objects:  91% (551/605)
2025-03-14T04:20:44.6425543Z remote: Compressing objects:  92% (557/605)
2025-03-14T04:20:44.6425829Z remote: Compressing objects:  93% (563/605)
2025-03-14T04:20:44.6433141Z remote: Compressing objects:  94% (569/605)
2025-03-14T04:20:44.6433590Z remote: Compressing objects:  95% (575/605)
2025-03-14T04:20:44.6441327Z remote: Compressing objects:  96% (581/605)
2025-03-14T04:20:44.6446064Z remote: Compressing objects:  97% (587/605)
2025-03-14T04:20:44.6447837Z remote: Compressing objects:  98% (593/605)
2025-03-14T04:20:44.6448149Z remote: Compressing objects:  99% (599/605)
2025-03-14T04:20:44.6448473Z remote: Compressing objects: 100% (605/605)
2025-03-14T04:20:44.6448802Z remote: Compressing objects: 100% (605/605), done.
2025-03-14T04:20:44.6511704Z Receiving objects:   0% (1/35366)
2025-03-14T04:20:44.6564013Z Receiving objects:   1% (354/35366)
2025-03-14T04:20:44.6610939Z Receiving objects:   2% (708/35366)
2025-03-14T04:20:44.6641155Z Receiving objects:   3% (1061/35366)
2025-03-14T04:20:44.6672051Z Receiving objects:   4% (1415/35366)
2025-03-14T04:20:44.6712210Z Receiving objects:   5% (1769/35366)
2025-03-14T04:20:44.6730983Z Receiving objects:   6% (2122/35366)
2025-03-14T04:20:44.6763383Z Receiving objects:   7% (2476/35366)
2025-03-14T04:20:44.6794320Z Receiving objects:   8% (2830/35366)
2025-03-14T04:20:44.6839288Z Receiving objects:   9% (3183/35366)
2025-03-14T04:20:44.6888779Z Receiving objects:  10% (3537/35366)
2025-03-14T04:20:44.6954790Z Receiving objects:  11% (3891/35366)
2025-03-14T04:20:44.7054183Z Receiving objects:  12% (4244/35366)
2025-03-14T04:20:44.7186600Z Receiving objects:  13% (4598/35366)
2025-03-14T04:20:44.7249893Z Receiving objects:  14% (4952/35366)
2025-03-14T04:20:44.7290806Z Receiving objects:  15% (5305/35366)
2025-03-14T04:20:44.7330223Z Receiving objects:  16% (5659/35366)
2025-03-14T04:20:44.7354789Z Receiving objects:  17% (6013/35366)
2025-03-14T04:20:44.7382629Z Receiving objects:  18% (6366/35366)
2025-03-14T04:20:44.7402547Z Receiving objects:  19% (6720/35366)
2025-03-14T04:20:44.9565547Z Receiving objects:  20% (7074/35366)
2025-03-14T04:20:45.6449497Z Receiving objects:  21% (7427/35366)
2025-03-14T04:20:45.9843662Z Receiving objects:  21% (7674/35366), 97.01 MiB | 97.00 MiB/s
2025-03-14T04:20:46.0531388Z Receiving objects:  22% (7781/35366), 97.01 MiB | 97.00 MiB/s
2025-03-14T04:20:46.1201932Z Receiving objects:  23% (8135/35366), 97.01 MiB | 97.00 MiB/s
2025-03-14T04:20:46.1874214Z Receiving objects:  24% (8488/35366), 97.01 MiB | 97.00 MiB/s
2025-03-14T04:20:46.2528202Z Receiving objects:  25% (8842/35366), 138.48 MiB | 92.31 MiB/s
2025-03-14T04:20:46.3191736Z Receiving objects:  26% (9196/35366), 138.48 MiB | 92.31 MiB/s
2025-03-14T04:20:46.3860359Z Receiving objects:  27% (9549/35366), 138.48 MiB | 92.31 MiB/s
2025-03-14T04:20:46.4521376Z Receiving objects:  28% (9903/35366), 138.48 MiB | 92.31 MiB/s
2025-03-14T04:20:46.6449915Z Receiving objects:  29% (10257/35366), 138.48 MiB | 92.31 MiB/s
2025-03-14T04:20:46.6697943Z Receiving objects:  29% (10579/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:46.6812552Z Receiving objects:  30% (10610/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:46.9313134Z Receiving objects:  31% (10964/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.0994895Z Receiving objects:  32% (11318/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1006461Z Receiving objects:  33% (11671/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1023085Z Receiving objects:  34% (12025/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1039431Z Receiving objects:  35% (12379/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1039781Z Receiving objects:  36% (12732/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1056805Z Receiving objects:  37% (13086/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1070213Z Receiving objects:  38% (13440/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1070671Z Receiving objects:  39% (13793/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1086965Z Receiving objects:  40% (14147/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1100743Z Receiving objects:  41% (14501/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1279435Z Receiving objects:  42% (14854/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1282456Z Receiving objects:  43% (15208/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1284309Z Receiving objects:  44% (15562/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1297818Z Receiving objects:  45% (15915/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1309944Z Receiving objects:  46% (16269/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1326533Z Receiving objects:  47% (16623/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1332133Z Receiving objects:  48% (16976/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1448483Z Receiving objects:  49% (17330/35366), 183.06 MiB | 91.53 MiB/s
2025-03-14T04:20:47.1450620Z Receiving objects:  50% (17683/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.1463520Z Receiving objects:  51% (18037/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.1681105Z Receiving objects:  52% (18391/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2040502Z Receiving objects:  53% (18744/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2043579Z Receiving objects:  54% (19098/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2062075Z Receiving objects:  55% (19452/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2066962Z Receiving objects:  56% (19805/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2077068Z Receiving objects:  57% (20159/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2095953Z Receiving objects:  58% (20513/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2104957Z Receiving objects:  59% (20866/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2111075Z Receiving objects:  60% (21220/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2297311Z Receiving objects:  61% (21574/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2317436Z Receiving objects:  62% (21927/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2333268Z Receiving objects:  63% (22281/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2342350Z Receiving objects:  64% (22635/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2380640Z Receiving objects:  65% (22988/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2487003Z Receiving objects:  66% (23342/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2491395Z Receiving objects:  67% (23696/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2503586Z Receiving objects:  68% (24049/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2512189Z Receiving objects:  69% (24403/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2516371Z Receiving objects:  70% (24757/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2606432Z Receiving objects:  71% (25110/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2617349Z Receiving objects:  72% (25464/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2627377Z Receiving objects:  73% (25818/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2643647Z Receiving objects:  74% (26171/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2670891Z Receiving objects:  75% (26525/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2685398Z Receiving objects:  76% (26879/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2703730Z Receiving objects:  77% (27232/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2718509Z Receiving objects:  78% (27586/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2738908Z Receiving objects:  79% (27940/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2820732Z Receiving objects:  80% (28293/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2831210Z Receiving objects:  81% (28647/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2843603Z Receiving objects:  82% (29001/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2877885Z Receiving objects:  83% (29354/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2949347Z Receiving objects:  84% (29708/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2957364Z Receiving objects:  85% (30062/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2957806Z Receiving objects:  86% (30415/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2959975Z Receiving objects:  87% (30769/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2964672Z Receiving objects:  88% (31123/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2965111Z Receiving objects:  89% (31476/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2967320Z Receiving objects:  90% (31830/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2971733Z Receiving objects:  91% (32184/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2980989Z Receiving objects:  92% (32537/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2985909Z Receiving objects:  93% (32891/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.2991409Z Receiving objects:  94% (33245/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.3004985Z Receiving objects:  95% (33598/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.3062501Z Receiving objects:  96% (33952/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.3075547Z Receiving objects:  97% (34306/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.3109463Z Receiving objects:  98% (34659/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.3148938Z Receiving objects:  99% (35013/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.3152907Z remote: Total 35366 (delta 5141), reused 4845 (delta 4838), pack-reused 29923 (from 2)
2025-03-14T04:20:47.3160772Z Receiving objects: 100% (35366/35366), 224.64 MiB | 89.85 MiB/s
2025-03-14T04:20:47.3165352Z Receiving objects: 100% (35366/35366), 233.84 MiB | 87.55 MiB/s, done.
2025-03-14T04:20:47.3198484Z Resolving deltas:   0% (0/19971)
2025-03-14T04:20:47.3237423Z Resolving deltas:   1% (202/19971)
2025-03-14T04:20:47.3246477Z Resolving deltas:   2% (400/19971)
2025-03-14T04:20:47.3351894Z Resolving deltas:   3% (600/19971)
2025-03-14T04:20:47.3365020Z Resolving deltas:   4% (799/19971)
2025-03-14T04:20:47.3368003Z Resolving deltas:   5% (999/19971)
2025-03-14T04:20:47.3380465Z Resolving deltas:   6% (1199/19971)
2025-03-14T04:20:47.3389311Z Resolving deltas:   7% (1398/19971)
2025-03-14T04:20:47.3393306Z Resolving deltas:   8% (1598/19971)
2025-03-14T04:20:47.3400570Z Resolving deltas:   9% (1798/19971)
2025-03-14T04:20:47.3407658Z Resolving deltas:  10% (1998/19971)
2025-03-14T04:20:47.3413859Z Resolving deltas:  11% (2197/19971)
2025-03-14T04:20:47.3419793Z Resolving deltas:  12% (2397/19971)
2025-03-14T04:20:47.3425064Z Resolving deltas:  13% (2597/19971)
2025-03-14T04:20:47.3430601Z Resolving deltas:  14% (2796/19971)
2025-03-14T04:20:47.3434567Z Resolving deltas:  15% (2996/19971)
2025-03-14T04:20:47.3440616Z Resolving deltas:  16% (3196/19971)
2025-03-14T04:20:47.3447709Z Resolving deltas:  17% (3396/19971)
2025-03-14T04:20:47.3454837Z Resolving deltas:  18% (3595/19971)
2025-03-14T04:20:47.3462250Z Resolving deltas:  19% (3795/19971)
2025-03-14T04:20:47.3470191Z Resolving deltas:  20% (3995/19971)
2025-03-14T04:20:47.3477088Z Resolving deltas:  21% (4194/19971)
2025-03-14T04:20:47.3480950Z Resolving deltas:  22% (4394/19971)
2025-03-14T04:20:47.3489010Z Resolving deltas:  23% (4594/19971)
2025-03-14T04:20:47.3490440Z Resolving deltas:  24% (4794/19971)
2025-03-14T04:20:47.3517977Z Resolving deltas:  25% (4993/19971)
2025-03-14T04:20:47.3526411Z Resolving deltas:  26% (5193/19971)
2025-03-14T04:20:47.3526768Z Resolving deltas:  27% (5393/19971)
2025-03-14T04:20:47.3533848Z Resolving deltas:  28% (5592/19971)
2025-03-14T04:20:47.3539273Z Resolving deltas:  29% (5795/19971)
2025-03-14T04:20:47.3545268Z Resolving deltas:  30% (5992/19971)
2025-03-14T04:20:47.3551744Z Resolving deltas:  31% (6193/19971)
2025-03-14T04:20:47.3558860Z Resolving deltas:  32% (6391/19971)
2025-03-14T04:20:47.3574206Z Resolving deltas:  33% (6591/19971)
2025-03-14T04:20:47.3587528Z Resolving deltas:  34% (6791/19971)
2025-03-14T04:20:47.3587832Z Resolving deltas:  35% (6991/19971)
2025-03-14T04:20:47.3590706Z Resolving deltas:  36% (7190/19971)
2025-03-14T04:20:47.3615955Z Resolving deltas:  37% (7392/19971)
2025-03-14T04:20:47.3623428Z Resolving deltas:  38% (7589/19971)
2025-03-14T04:20:47.3629565Z Resolving deltas:  39% (7789/19971)
2025-03-14T04:20:47.3635440Z Resolving deltas:  40% (7989/19971)
2025-03-14T04:20:47.3643080Z Resolving deltas:  41% (8189/19971)
2025-03-14T04:20:47.3652082Z Resolving deltas:  42% (8388/19971)
2025-03-14T04:20:47.3655860Z Resolving deltas:  43% (8589/19971)
2025-03-14T04:20:47.3673054Z Resolving deltas:  44% (8788/19971)
2025-03-14T04:20:47.3673322Z Resolving deltas:  45% (8988/19971)
2025-03-14T04:20:47.3683810Z Resolving deltas:  46% (9187/19971)
2025-03-14T04:20:47.3687788Z Resolving deltas:  47% (9387/19971)
2025-03-14T04:20:47.3705740Z Resolving deltas:  48% (9587/19971)
2025-03-14T04:20:47.3708227Z Resolving deltas:  49% (9795/19971)
2025-03-14T04:20:47.3710492Z Resolving deltas:  50% (9986/19971)
2025-03-14T04:20:47.3717057Z Resolving deltas:  51% (10186/19971)
2025-03-14T04:20:47.3771613Z Resolving deltas:  52% (10385/19971)
2025-03-14T04:20:47.3784054Z Resolving deltas:  53% (10585/19971)
2025-03-14T04:20:47.3797324Z Resolving deltas:  54% (10785/19971)
2025-03-14T04:20:47.3801605Z Resolving deltas:  55% (10985/19971)
2025-03-14T04:20:47.3826648Z Resolving deltas:  56% (11184/19971)
2025-03-14T04:20:47.3831596Z Resolving deltas:  57% (11384/19971)
2025-03-14T04:20:47.3837815Z Resolving deltas:  58% (11584/19971)
2025-03-14T04:20:47.3846257Z Resolving deltas:  59% (11783/19971)
2025-03-14T04:20:47.3854770Z Resolving deltas:  60% (11983/19971)
2025-03-14T04:20:47.3868299Z Resolving deltas:  61% (12183/19971)
2025-03-14T04:20:47.3873558Z Resolving deltas:  62% (12383/19971)
2025-03-14T04:20:47.3882793Z Resolving deltas:  63% (12582/19971)
2025-03-14T04:20:47.3887246Z Resolving deltas:  64% (12783/19971)
2025-03-14T04:20:47.3912085Z Resolving deltas:  65% (12982/19971)
2025-03-14T04:20:47.3914940Z Resolving deltas:  66% (13181/19971)
2025-03-14T04:20:47.3924475Z Resolving deltas:  67% (13381/19971)
2025-03-14T04:20:47.3930292Z Resolving deltas:  68% (13581/19971)
2025-03-14T04:20:47.3936598Z Resolving deltas:  69% (13781/19971)
2025-03-14T04:20:47.3944418Z Resolving deltas:  70% (13980/19971)
2025-03-14T04:20:47.3950710Z Resolving deltas:  71% (14180/19971)
2025-03-14T04:20:47.3957436Z Resolving deltas:  72% (14380/19971)
2025-03-14T04:20:47.3970431Z Resolving deltas:  73% (14579/19971)
2025-03-14T04:20:47.3982931Z Resolving deltas:  74% (14779/19971)
2025-03-14T04:20:47.3992881Z Resolving deltas:  75% (14979/19971)
2025-03-14T04:20:47.3996703Z Resolving deltas:  76% (15178/19971)
2025-03-14T04:20:47.4005630Z Resolving deltas:  77% (15378/19971)
2025-03-14T04:20:47.4017917Z Resolving deltas:  78% (15578/19971)
2025-03-14T04:20:47.4027164Z Resolving deltas:  79% (15778/19971)
2025-03-14T04:20:47.4037078Z Resolving deltas:  80% (15977/19971)
2025-03-14T04:20:47.4043754Z Resolving deltas:  81% (16177/19971)
2025-03-14T04:20:47.4050649Z Resolving deltas:  82% (16378/19971)
2025-03-14T04:20:47.4062269Z Resolving deltas:  83% (16576/19971)
2025-03-14T04:20:47.4075238Z Resolving deltas:  84% (16777/19971)
2025-03-14T04:20:47.4086134Z Resolving deltas:  85% (16976/19971)
2025-03-14T04:20:47.4101844Z Resolving deltas:  86% (17176/19971)
2025-03-14T04:20:47.4108090Z Resolving deltas:  87% (17375/19971)
2025-03-14T04:20:47.4116697Z Resolving deltas:  88% (17575/19971)
2025-03-14T04:20:47.4127141Z Resolving deltas:  89% (17775/19971)
2025-03-14T04:20:47.4139063Z Resolving deltas:  90% (17974/19971)
2025-03-14T04:20:47.4147118Z Resolving deltas:  91% (18174/19971)
2025-03-14T04:20:47.4154833Z Resolving deltas:  92% (18374/19971)
2025-03-14T04:20:47.4158692Z Resolving deltas:  93% (18574/19971)
2025-03-14T04:20:47.4166439Z Resolving deltas:  94% (18773/19971)
2025-03-14T04:20:47.4168288Z Resolving deltas:  95% (18974/19971)
2025-03-14T04:20:47.4172064Z Resolving deltas:  96% (19173/19971)
2025-03-14T04:20:47.4181215Z Resolving deltas:  97% (19372/19971)
2025-03-14T04:20:47.4196930Z Resolving deltas:  98% (19572/19971)
2025-03-14T04:20:47.4269410Z Resolving deltas:  99% (19772/19971)
2025-03-14T04:20:47.4269922Z Resolving deltas: 100% (19971/19971)
2025-03-14T04:20:47.4270288Z Resolving deltas: 100% (19971/19971), done.
2025-03-14T04:20:48.3811682Z + pushd torchbench
2025-03-14T04:20:48.3812021Z ~/workspace/torchbench ~/workspace
2025-03-14T04:20:48.3812319Z + git checkout 373ffb19dc470f4423a3176a4133f8f4b3cdb5bd
2025-03-14T04:20:48.4091987Z Note: switching to '373ffb19dc470f4423a3176a4133f8f4b3cdb5bd'.
2025-03-14T04:20:48.4092274Z 
2025-03-14T04:20:48.4092478Z You are in 'detached HEAD' state. You can look around, make experimental
2025-03-14T04:20:48.4092871Z changes and commit them, and you can discard any commits you make in this
2025-03-14T04:20:48.4093287Z state without impacting any branches by switching back to a branch.
2025-03-14T04:20:48.4093534Z 
2025-03-14T04:20:48.4093694Z If you want to create a new branch to retain commits you create, you may
2025-03-14T04:20:48.4094113Z do so (now or later) by using -c with the switch command. Example:
2025-03-14T04:20:48.4094346Z 
2025-03-14T04:20:48.4094463Z   git switch -c <new-branch-name>
2025-03-14T04:20:48.4094627Z 
2025-03-14T04:20:48.4094736Z Or undo this operation with:
2025-03-14T04:20:48.4094924Z 
2025-03-14T04:20:48.4095011Z   git switch -
2025-03-14T04:20:48.4095132Z 
2025-03-14T04:20:48.4095293Z Turn off this advice by setting config variable advice.detachedHead to false
2025-03-14T04:20:48.4095553Z 
2025-03-14T04:20:48.4095723Z HEAD is now at 373ffb19 Copy model before benchmark warmup runs (#145858)
2025-03-14T04:20:48.4096014Z + '[' '' ']'
2025-03-14T04:20:48.4096243Z + python install.py --continue_on_fail
2025-03-14T04:20:53.0107972Z checking packages numpy, torch, torchvision, torchaudio are installed, generating constaints...OK
2025-03-14T04:21:15.6722832Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/BERT_pytorch...OK
2025-03-14T04:21:26.1231395Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/Background_Matting...OK
2025-03-14T04:21:36.7990460Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/LearningToPaint...OK
2025-03-14T04:21:47.2725340Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/Super_SloMo...OK
2025-03-14T04:21:56.4797067Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/alexnet...OK
2025-03-14T04:22:11.1250017Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_edgecnn...OK
2025-03-14T04:22:22.2286891Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_gcn...OK
2025-03-14T04:22:33.3035946Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_gin...OK
2025-03-14T04:22:44.4192012Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_sage...OK
2025-03-14T04:22:44.4198086Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/cm3leon_generate...SKIP - No install.py is found
2025-03-14T04:22:54.4363852Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/dcgan...OK
2025-03-14T04:23:05.4231432Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/demucs...OK
2025-03-14T04:23:14.5190156Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/densenet121...OK
2025-03-14T04:23:52.1809646Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_101_c4...OK
2025-03-14T04:24:09.0427573Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_101_dc5...OK
2025-03-14T04:24:24.7376292Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_101_fpn...OK
2025-03-14T04:24:40.5285472Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_50_c4...OK
2025-03-14T04:24:57.8121002Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_50_dc5...OK
2025-03-14T04:25:13.3759114Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_50_fpn...OK
2025-03-14T04:25:29.0084003Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fcos_r_50_fpn...OK
2025-03-14T04:25:44.3807231Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn...OK
2025-03-14T04:25:59.9833020Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_101_c4...OK
2025-03-14T04:26:15.8539249Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_101_fpn...OK
2025-03-14T04:26:31.0098660Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_50_c4...OK
2025-03-14T04:26:46.8207101Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_50_fpn...OK
2025-03-14T04:26:57.0978936Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/dlrm...OK
2025-03-14T04:27:22.2944797Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/doctr_det_predictor...OK
2025-03-14T04:27:37.2538149Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/doctr_reco_predictor...OK
2025-03-14T04:27:50.6857077Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/drq...OK
2025-03-14T04:28:07.6759774Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/fastNLP_Bert...OK
2025-03-14T04:28:18.1497301Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/functorch_dp_cifar10...OK
2025-03-14T04:28:28.8283792Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/functorch_maml_omniglot...OK
2025-03-14T04:28:44.0583391Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Albert...OK
2025-03-14T04:28:59.5232447Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Bart...OK
2025-03-14T04:29:13.8897284Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Bert...OK
2025-03-14T04:29:29.6952266Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Bert_large...OK
2025-03-14T04:29:44.2826471Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_BigBird...OK
2025-03-14T04:29:58.7673399Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_DistilBert...OK
2025-03-14T04:30:14.1177364Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_GPT2...OK
2025-03-14T04:30:35.4550131Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_GPT2_large...OK
2025-03-14T04:30:51.0975448Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Longformer...OK
2025-03-14T04:31:04.7861036Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Reformer...OK
2025-03-14T04:31:22.1400577Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Roberta_base...OK
2025-03-14T04:31:36.3499360Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5...OK
2025-03-14T04:31:51.9734387Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5_base...OK
2025-03-14T04:31:51.9735142Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5_generate...SKIP - No install.py is found
2025-03-14T04:32:11.7814960Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5_large...OK
2025-03-14T04:32:24.6446001Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Whisper...OK
2025-03-14T04:32:24.6449352Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_clip...SKIP - No install.py is found
2025-03-14T04:32:40.6054683Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_distil_whisper...OK
2025-03-14T04:32:50.9827692Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/lennard_jones...OK
2025-03-14T04:33:01.4129765Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/llama...OK
2025-03-14T04:33:40.0985913Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/llama_v2_7b_16h...OK
2025-03-14T04:34:46.4758270Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/llava...OK
2025-03-14T04:34:55.9961224Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/maml...OK
2025-03-14T04:35:06.7549154Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/maml_omniglot...OK
2025-03-14T04:35:06.7552078Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/microbench_unbacked_tolist_sum...SKIP - No install.py is found
2025-03-14T04:35:16.2618337Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mnasnet1_0...OK
2025-03-14T04:35:25.8112065Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mobilenet_v2...OK
2025-03-14T04:35:35.3196050Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mobilenet_v2_quantized_qat...OK
2025-03-14T04:35:44.9032233Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mobilenet_v3_large...OK
2025-03-14T04:35:54.4250255Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/moco...OK
2025-03-14T04:36:18.9123731Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/moondream...OK
2025-03-14T04:36:18.9125916Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/nanogpt...SKIP - No install.py is found
2025-03-14T04:36:29.5488435Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/nvidia_deeprecommender...OK
2025-03-14T04:36:41.0611932Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/opacus_cifar10...OK
2025-03-14T04:36:50.5298903Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/phlippe_densenet...OK
2025-03-14T04:37:00.0100354Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/phlippe_resnet...OK
2025-03-14T04:37:09.4540187Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pyhpc_equation_of_state...OK
2025-03-14T04:37:18.9646773Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pyhpc_isoneutral_mixing...OK
2025-03-14T04:37:28.5126758Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pyhpc_turbulent_kinetic_energy...OK
2025-03-14T04:37:42.9310084Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pytorch_CycleGAN_and_pix2pix...OK
2025-03-14T04:37:53.5535623Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pytorch_stargan...OK
2025-03-14T04:38:05.9846815Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pytorch_unet...OK
2025-03-14T04:38:15.4406409Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet152...OK
2025-03-14T04:38:24.8853232Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet18...OK
2025-03-14T04:38:34.2841176Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet50...OK
2025-03-14T04:38:43.7606599Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet50_quantized_qat...OK
2025-03-14T04:38:53.3704681Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnext50_32x4d...OK
2025-03-14T04:39:11.2273588Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/sam...OK
2025-03-14T04:39:29.7984131Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/sam_fast...OK
2025-03-14T04:39:39.2024375Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/shufflenet_v2_x1_0...OK
2025-03-14T04:39:39.2031355Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/simple_gpt...SKIP - No install.py is found
2025-03-14T04:39:39.2032113Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/simple_gpt_tp_manual...SKIP - No install.py is found
2025-03-14T04:39:50.9522440Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/soft_actor_critic...OK
2025-03-14T04:40:02.0127625Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/speech_transformer...OK
2025-03-14T04:40:11.3676389Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/squeezenet1_1...OK
2025-03-14T04:40:42.6632264Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/stable_diffusion_text_encoder...OK
2025-03-14T04:40:57.1318435Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/stable_diffusion_unet...OK
2025-03-14T04:41:11.7022331Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/tacotron2...OK
2025-03-14T04:41:27.4103593Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_efficientdet...OK
2025-03-14T04:41:36.7466737Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_efficientnet...OK
2025-03-14T04:41:46.0306980Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_nfnet...OK
2025-03-14T04:41:55.3290616Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_regnet...OK
2025-03-14T04:42:04.7070289Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_resnest...OK
2025-03-14T04:42:14.0287912Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_vision_transformer...OK
2025-03-14T04:42:23.2954271Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_vision_transformer_large...OK
2025-03-14T04:42:32.6491687Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_vovnet...OK
2025-03-14T04:42:46.7019523Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/torch_multimodal_clip...OK
2025-03-14T04:43:00.1619319Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/tts_angular...OK
2025-03-14T04:43:09.4609265Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/vgg16...OK
2025-03-14T04:43:20.2155837Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/vision_maskrcnn...OK
2025-03-14T04:43:31.1307286Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/yolov3...OK
2025-03-14T04:43:35.7570189Z installed torchbench with package constraints: {'numpy': '1.22.4', 'torch': '2.8.0a0+gitaed0b7a', 'torchvision': '0.19.0a0+d23a6e1', 'torchaudio': '2.6.0a0+c670ad8'}
2025-03-14T04:43:35.9861755Z + pip install transformers==4.38.1
2025-03-14T04:43:36.3328796Z Collecting transformers==4.38.1
2025-03-14T04:43:36.3437266Z   Downloading transformers-4.38.1-py3-none-any.whl.metadata (131 kB)
2025-03-14T04:43:36.4835701Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (3.16.1)
2025-03-14T04:43:36.4841118Z Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (0.29.3)
2025-03-14T04:43:36.4846469Z Requirement already satisfied: numpy>=1.17 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (1.22.4)
2025-03-14T04:43:36.4848422Z Requirement already satisfied: packaging>=20.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (24.2)
2025-03-14T04:43:36.4849503Z Requirement already satisfied: pyyaml>=5.1 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (6.0.2)
2025-03-14T04:43:36.4850135Z Requirement already satisfied: regex!=2019.12.17 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (2024.11.6)
2025-03-14T04:43:36.4850892Z Requirement already satisfied: requests in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (2.32.3)
2025-03-14T04:43:36.5778083Z Collecting tokenizers<0.19,>=0.14 (from transformers==4.38.1)
2025-03-14T04:43:36.5789497Z   Using cached tokenizers-0.15.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)
2025-03-14T04:43:36.5812689Z Requirement already satisfied: safetensors>=0.4.1 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (0.5.3)
2025-03-14T04:43:36.5813372Z Requirement already satisfied: tqdm>=4.27 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (4.67.1)
2025-03-14T04:43:36.5966342Z Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.38.1) (2024.10.0)
2025-03-14T04:43:36.5971826Z Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.38.1) (4.12.2)
2025-03-14T04:43:36.6097522Z Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (3.4.1)
2025-03-14T04:43:36.6103657Z Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (3.10)
2025-03-14T04:43:36.6105733Z Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (1.26.20)
2025-03-14T04:43:36.6106473Z Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (2025.1.31)
2025-03-14T04:43:36.6437601Z Downloading transformers-4.38.1-py3-none-any.whl (8.5 MB)
2025-03-14T04:43:36.6981235Z [?25l   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/8.5 MB ? eta -:--:--
2025-03-14T04:43:36.6982002Z    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 8.5/8.5 MB 167.0 MB/s eta 0:00:00
2025-03-14T04:43:36.6984645Z [?25hUsing cached tokenizers-0.15.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)
2025-03-14T04:43:37.4590765Z Installing collected packages: tokenizers, transformers
2025-03-14T04:43:37.4591102Z   Attempting uninstall: tokenizers
2025-03-14T04:43:37.4603455Z     Found existing installation: tokenizers 0.19.1
2025-03-14T04:43:37.4633423Z     Uninstalling tokenizers-0.19.1:
2025-03-14T04:43:37.4639847Z       Successfully uninstalled tokenizers-0.19.1
2025-03-14T04:43:37.5275311Z   Attempting uninstall: transformers
2025-03-14T04:43:37.5289473Z     Found existing installation: transformers 4.44.2
2025-03-14T04:43:37.6342600Z     Uninstalling transformers-4.44.2:
2025-03-14T04:43:37.6525476Z       Successfully uninstalled transformers-4.44.2
2025-03-14T04:43:40.6620687Z Successfully installed tokenizers-0.15.2 transformers-4.38.1
2025-03-14T04:43:40.7746417Z + echo 'Print all dependencies after TorchBench is installed'
2025-03-14T04:43:40.7746943Z Print all dependencies after TorchBench is installed
2025-03-14T04:43:40.7747340Z + python -mpip freeze
2025-03-14T04:43:41.0996091Z absl-py==2.1.0
2025-03-14T04:43:41.0996425Z accelerate==1.5.1
2025-03-14T04:43:41.0996619Z aiohappyeyeballs==2.6.1
2025-03-14T04:43:41.0996820Z aiohttp==3.11.13
2025-03-14T04:43:41.0996996Z aiosignal==1.3.2
2025-03-14T04:43:41.0997159Z alabaster==0.7.16
2025-03-14T04:43:41.0997340Z annotated-types==0.7.0
2025-03-14T04:43:41.0997545Z antlr4-python3-runtime==4.9.3
2025-03-14T04:43:41.0997754Z anyascii==0.3.2
2025-03-14T04:43:41.0997925Z astroid==3.3.9
2025-03-14T04:43:41.0998452Z asttokens==3.0.0
2025-03-14T04:43:41.0998642Z astunparse==1.6.3
2025-03-14T04:43:41.0998820Z async-timeout==5.0.1
2025-03-14T04:43:41.0999005Z attrs==23.1.0
2025-03-14T04:43:41.0999174Z audioread==3.0.1
2025-03-14T04:43:41.0999459Z babel==2.17.0
2025-03-14T04:43:41.0999623Z backcall==0.2.0
2025-03-14T04:43:41.0999797Z beautifulsoup4==4.13.3
2025-03-14T04:43:41.1000361Z -e git+https://github.com/pytorch/benchmark@373ffb19dc470f4423a3176a4133f8f4b3cdb5bd#egg=bert_pytorch&subdirectory=torchbenchmark/models/BERT_pytorch
2025-03-14T04:43:41.1000848Z black==25.1.0
2025-03-14T04:43:41.1001004Z blinker==1.9.0
2025-03-14T04:43:41.1001173Z blis==1.2.0
2025-03-14T04:43:41.1001335Z blobfile==3.0.0
2025-03-14T04:43:41.1001505Z bokeh==3.4.3
2025-03-14T04:43:41.1001664Z boto3==1.35.42
2025-03-14T04:43:41.1001831Z botocore==1.35.99
2025-03-14T04:43:41.1002000Z breathe==4.34.0
2025-03-14T04:43:41.1002162Z bs4==0.0.1
2025-03-14T04:43:41.1002324Z cachetools==5.5.2
2025-03-14T04:43:41.1002502Z cardboardlint==1.3.1
2025-03-14T04:43:41.1002680Z catalogue==2.0.10
2025-03-14T04:43:41.1002848Z certifi==2025.1.31
2025-03-14T04:43:41.1003016Z cffi==1.17.1
2025-03-14T04:43:41.1003184Z charset-normalizer==3.4.1
2025-03-14T04:43:41.1003379Z click==8.1.8
2025-03-14T04:43:41.1003545Z cloudpathlib==0.21.0
2025-03-14T04:43:41.1003725Z cloudpickle==3.1.1
2025-03-14T04:43:41.1003895Z colorama==0.4.6
2025-03-14T04:43:41.1004061Z comm==0.2.2
2025-03-14T04:43:41.1004217Z confection==0.1.5
2025-03-14T04:43:41.1004386Z contourpy==1.2.1
2025-03-14T04:43:41.1004558Z coremltools==5.0b5
2025-03-14T04:43:41.1004733Z cryptography==44.0.2
2025-03-14T04:43:41.1004905Z csvw==3.5.1
2025-03-14T04:43:41.1005060Z cycler==0.12.1
2025-03-14T04:43:41.1005222Z cymem==2.0.11
2025-03-14T04:43:41.1005383Z Cython==3.0.12
2025-03-14T04:43:41.1005538Z DALL-E==0.1
2025-03-14T04:43:41.1005705Z dataclasses-json==0.6.7
2025-03-14T04:43:41.1005888Z datasets==3.3.2
2025-03-14T04:43:41.1006054Z debugpy==1.8.13
2025-03-14T04:43:41.1006219Z decorator==5.2.1
2025-03-14T04:43:41.1006401Z defusedxml==0.7.1
2025-03-14T04:43:41.1006568Z Deprecated==1.2.18
2025-03-14T04:43:41.1006910Z detectron2 @ git+https://github.com/facebookresearch/detectron2.git@0df2d73d0013db7de629602c23cc120219b4f2b8
2025-03-14T04:43:41.1007291Z diffusers==0.30.3
2025-03-14T04:43:41.1007460Z dill==0.3.8
2025-03-14T04:43:41.1007618Z diskcache==5.6.3
2025-03-14T04:43:41.1007785Z distro==1.9.0
2025-03-14T04:43:41.1007948Z dlinfo==2.0.0
2025-03-14T04:43:41.1008113Z dnspython==2.7.0
2025-03-14T04:43:41.1008282Z docker-pycreds==0.4.0
2025-03-14T04:43:41.1008458Z docutils==0.16
2025-03-14T04:43:41.1008617Z dominate==2.9.1
2025-03-14T04:43:41.1008951Z effdet @ git+https://github.com/rwightman/efficientdet-pytorch.git@d43c9e34cd62d22b4205831bb735f6dd83b8e881
2025-03-14T04:43:41.1009311Z einops==0.8.1
2025-03-14T04:43:41.1014349Z eval_type_backport==0.2.2
2025-03-14T04:43:41.1014582Z exceptiongroup==1.2.2
2025-03-14T04:43:41.1014766Z execnet==2.1.1
2025-03-14T04:43:41.1014942Z executing==2.2.0
2025-03-14T04:43:41.1015129Z exhale==0.2.3
2025-03-14T04:43:41.1015293Z expecttest==0.3.0
2025-03-14T04:43:41.1015470Z fastjsonschema==2.21.1
2025-03-14T04:43:41.1015651Z FastNLP==0.6.0
2025-03-14T04:43:41.1015831Z fbscribelogger==0.1.7
2025-03-14T04:43:41.1016013Z ffmpeg-python==0.2.0
2025-03-14T04:43:41.1016192Z filelock==3.16.1
2025-03-14T04:43:41.1016357Z Flask==3.1.0
2025-03-14T04:43:41.1016521Z flatbuffers==2.0
2025-03-14T04:43:41.1016691Z fonttools==4.56.0
2025-03-14T04:43:41.1016858Z frozenlist==1.5.0
2025-03-14T04:43:41.1017025Z fsspec==2024.10.0
2025-03-14T04:43:41.1017185Z ftfy==6.3.1
2025-03-14T04:43:41.1017504Z functorch @ git+https://github.com/pytorch/functorch.git@b71aa0b4387b86c278132209b99538be48ef4c74
2025-03-14T04:43:41.1017848Z future==1.0.0
2025-03-14T04:43:41.1018016Z fvcore==0.1.5.post20221221
2025-03-14T04:43:41.1018198Z gdown==5.2.0
2025-03-14T04:43:41.1018353Z ghstack==0.8.0
2025-03-14T04:43:41.1018511Z gitdb==4.0.12
2025-03-14T04:43:41.1018662Z GitPython==3.1.44
2025-03-14T04:43:41.1018961Z google-auth==2.38.0
2025-03-14T04:43:41.1019154Z google-auth-oauthlib==1.0.0
2025-03-14T04:43:41.1019353Z greenlet==3.1.1
2025-03-14T04:43:41.1019518Z grpcio==1.71.0
2025-03-14T04:43:41.1019681Z gym==0.26.2
2025-03-14T04:43:41.1019895Z gym-notices==0.0.8
2025-03-14T04:43:41.1020064Z h5py==3.13.0
2025-03-14T04:43:41.1020224Z higher==0.2.1
2025-03-14T04:43:41.1020391Z huggingface-hub==0.29.3
2025-03-14T04:43:41.1020571Z hydra-core==1.3.2
2025-03-14T04:43:41.1020739Z hypothesis==5.35.1
2025-03-14T04:43:41.1020907Z idna==3.10
2025-03-14T04:43:41.1021055Z imageio==2.37.0
2025-03-14T04:43:41.1021218Z imagesize==1.4.1
2025-03-14T04:43:41.1021391Z importlib_metadata==8.6.1
2025-03-14T04:43:41.1021576Z inflect==7.5.0
2025-03-14T04:43:41.1021737Z iniconfig==2.0.0
2025-03-14T04:43:41.1021901Z iopath==0.1.9
2025-03-14T04:43:41.1022060Z ipykernel==6.29.5
2025-03-14T04:43:41.1022222Z ipython==8.12.0
2025-03-14T04:43:41.1022380Z isodate==0.7.2
2025-03-14T04:43:41.1022534Z isort==6.0.1
2025-03-14T04:43:41.1022696Z itsdangerous==2.2.0
2025-03-14T04:43:41.1022863Z jedi==0.19.2
2025-03-14T04:43:41.1023016Z Jinja2==3.1.6
2025-03-14T04:43:41.1023171Z jmespath==1.0.1
2025-03-14T04:43:41.1023324Z joblib==1.4.2
2025-03-14T04:43:41.1023478Z jsonpatch==1.33
2025-03-14T04:43:41.1023644Z jsonpointer==3.0.0
2025-03-14T04:43:41.1023812Z jsonschema==4.23.0
2025-03-14T04:43:41.1023999Z jsonschema-specifications==2024.10.1
2025-03-14T04:43:41.1024217Z junitparser==2.1.1
2025-03-14T04:43:41.1024388Z jupyter-cache==0.6.1
2025-03-14T04:43:41.1024569Z jupyter_client==8.6.3
2025-03-14T04:43:41.1024749Z jupyter_core==5.7.2
2025-03-14T04:43:41.1024916Z kaldi-io==0.9.8
2025-03-14T04:43:41.1025079Z kiwisolver==1.4.7
2025-03-14T04:43:41.1025239Z kornia==0.8.0
2025-03-14T04:43:41.1025395Z kornia_rs==0.1.8
2025-03-14T04:43:41.1025548Z lameenc==1.8.1
2025-03-14T04:43:41.1025704Z langcodes==3.5.0
2025-03-14T04:43:41.1025865Z langdetect==1.0.9
2025-03-14T04:43:41.1026030Z language-tags==1.2.0
2025-03-14T04:43:41.1026202Z language_data==1.3.0
2025-03-14T04:43:41.1026371Z lark==0.12.0
2025-03-14T04:43:41.1026526Z lazy_loader==0.4
2025-03-14T04:43:41.1026686Z libcst==1.7.0
2025-03-14T04:43:41.1026840Z librosa==0.9.2
2025-03-14T04:43:41.1026999Z lintrunner==0.12.7
2025-03-14T04:43:41.1027164Z llvmlite==0.38.1
2025-03-14T04:43:41.1027324Z lxml==5.3.0
2025-03-14T04:43:41.1027479Z marisa-trie==1.2.1
2025-03-14T04:43:41.1027637Z Markdown==3.7
2025-03-14T04:43:41.1027801Z markdown-it-py==2.2.0
2025-03-14T04:43:41.1027980Z MarkupSafe==3.0.2
2025-03-14T04:43:41.1028148Z marshmallow==3.26.1
2025-03-14T04:43:41.1028317Z matplotlib==3.5.3
2025-03-14T04:43:41.1028584Z matplotlib-inline==0.1.7
2025-03-14T04:43:41.1028770Z mccabe==0.7.0
2025-03-14T04:43:41.1028936Z mdit-py-plugins==0.3.5
2025-03-14T04:43:41.1029108Z mdurl==0.1.2
2025-03-14T04:43:41.1029268Z ml_dtypes==0.5.1
2025-03-14T04:43:41.1029435Z MonkeyType==23.3.0
2025-03-14T04:43:41.1029637Z more-itertools==10.6.0
2025-03-14T04:43:41.1029809Z mpmath==1.3.0
2025-03-14T04:43:41.1029970Z msgpack==1.1.0
2025-03-14T04:43:41.1030136Z multidict==6.1.0
2025-03-14T04:43:41.1030308Z multiprocess==0.70.16
2025-03-14T04:43:41.1030486Z murmurhash==1.0.12
2025-03-14T04:43:41.1030653Z musdb==0.4.2
2025-03-14T04:43:41.1030811Z museval==0.4.1
2025-03-14T04:43:41.1030974Z mypy==1.14.0
2025-03-14T04:43:41.1031137Z mypy-extensions==1.0.0
2025-03-14T04:43:41.1031311Z myst-nb==0.17.2
2025-03-14T04:43:41.1031482Z myst-parser==0.18.1
2025-03-14T04:43:41.1031654Z nbclient==0.7.4
2025-03-14T04:43:41.1031819Z nbformat==5.10.4
2025-03-14T04:43:41.1031992Z nest-asyncio==1.6.0
2025-03-14T04:43:41.1032162Z networkx==2.8.8
2025-03-14T04:43:41.1032321Z ninja==1.10.2
2025-03-14T04:43:41.1032480Z nose==1.3.7
2025-03-14T04:43:41.1032637Z numba==0.55.2
2025-03-14T04:43:41.1032795Z numpy==1.22.4
2025-03-14T04:43:41.1032969Z nvidia-cublas-cu12==12.4.5.8
2025-03-14T04:43:41.1033180Z nvidia-cuda-cupti-cu12==12.4.127
2025-03-14T04:43:41.1033399Z nvidia-cuda-nvrtc-cu12==12.4.127
2025-03-14T04:43:41.1033615Z nvidia-cuda-runtime-cu12==12.4.127
2025-03-14T04:43:41.1033882Z nvidia-cudnn-cu12==9.1.0.70
2025-03-14T04:43:41.1034089Z nvidia-cufft-cu12==11.2.1.3
2025-03-14T04:43:41.1034292Z nvidia-curand-cu12==10.3.5.147
2025-03-14T04:43:41.1034499Z nvidia-cusolver-cu12==11.6.1.9
2025-03-14T04:43:41.1034776Z nvidia-cusparse-cu12==12.3.1.170
2025-03-14T04:43:41.1034991Z nvidia-cusparselt-cu12==0.6.2
2025-03-14T04:43:41.1035194Z nvidia-ml-py==12.570.86
2025-03-14T04:43:41.1035386Z nvidia-nccl-cu12==2.21.5
2025-03-14T04:43:41.1035586Z nvidia-nvjitlink-cu12==12.4.127
2025-03-14T04:43:41.1035797Z nvidia-nvtx-cu12==12.4.127
2025-03-14T04:43:41.1035986Z oauthlib==3.2.2
2025-03-14T04:43:41.1036157Z omegaconf==2.3.0
2025-03-14T04:43:41.1036327Z onnx==1.17.0
2025-03-14T04:43:41.1036491Z onnxscript==0.2.2
2025-03-14T04:43:41.1036652Z opacus==1.5.3
2025-03-14T04:43:41.1036822Z opencv-python==4.8.0.74
2025-03-14T04:43:41.1037008Z opt-einsum==3.3.0
2025-03-14T04:43:41.1037176Z optree==0.13.0
2025-03-14T04:43:41.1037343Z packaging==24.2
2025-03-14T04:43:41.1037518Z pandas==2.0.3
2025-03-14T04:43:41.1037685Z parameterized==0.8.1
2025-03-14T04:43:41.1037864Z parso==0.8.4
2025-03-14T04:43:41.1038026Z patch==1.16
2025-03-14T04:43:41.1038186Z pathspec==0.12.1
2025-03-14T04:43:41.1038355Z pexpect==4.9.0
2025-03-14T04:43:41.1038525Z phonemizer==3.3.0
2025-03-14T04:43:41.1038699Z pickleshare==0.7.5
2025-03-14T04:43:41.1038870Z pillow==11.0.0
2025-03-14T04:43:41.1039029Z platformdirs==4.3.6
2025-03-14T04:43:41.1039201Z pluggy==1.5.0
2025-03-14T04:43:41.1039364Z ply==3.11
2025-03-14T04:43:41.1039522Z pooch==1.8.2
2025-03-14T04:43:41.1039689Z portalocker==3.1.1
2025-03-14T04:43:41.1039859Z preshed==3.0.9
2025-03-14T04:43:41.1040028Z prettytable==3.15.1
2025-03-14T04:43:41.1040203Z prompt_toolkit==3.0.50
2025-03-14T04:43:41.1040385Z propcache==0.3.0
2025-03-14T04:43:41.1040553Z protobuf==3.20.2
2025-03-14T04:43:41.1040723Z psutil==7.0.0
2025-03-14T04:43:41.1040884Z ptyprocess==0.7.0
2025-03-14T04:43:41.1041055Z PuLP==2.9.0
2025-03-14T04:43:41.1041208Z pure_eval==0.2.3
2025-03-14T04:43:41.1041376Z pwlf==2.2.1
2025-03-14T04:43:41.1041544Z py-cpuinfo==9.0.0
2025-03-14T04:43:41.1041711Z pyaml==25.1.0
2025-03-14T04:43:41.1041871Z pyarrow==19.0.1
2025-03-14T04:43:41.1042037Z pyasn1==0.6.1
2025-03-14T04:43:41.1042215Z pyasn1_modules==0.4.1
2025-03-14T04:43:41.1042394Z pyclipper==1.3.0.post6
2025-03-14T04:43:41.1042569Z pycocotools==2.0.8
2025-03-14T04:43:41.1042734Z pycparser==2.22
2025-03-14T04:43:41.1042899Z pycryptodomex==3.21.0
2025-03-14T04:43:41.1043074Z pydantic==2.10.6
2025-03-14T04:43:41.1043241Z pydantic_core==2.27.2
2025-03-14T04:43:41.1043399Z pyDOE==0.3.8
2025-03-14T04:43:41.1043552Z pydot==3.0.4
2025-03-14T04:43:41.1043708Z pygame==2.6.1
2025-03-14T04:43:41.1043863Z PyGithub==2.3.0
2025-03-14T04:43:41.1044024Z Pygments==2.15.0
2025-03-14T04:43:41.1044186Z PyJWT==2.10.1
2025-03-14T04:43:41.1044340Z pylint==3.3.5
2025-03-14T04:43:41.1044494Z PyNaCl==1.5.0
2025-03-14T04:43:41.1044649Z pynvml==12.0.0
2025-03-14T04:43:41.1044807Z pyparsing==3.2.1
2025-03-14T04:43:41.1044969Z pypdfium2==4.30.1
2025-03-14T04:43:41.1045130Z pysbd==0.3.4
2025-03-14T04:43:41.1045279Z PySocks==1.7.1
2025-03-14T04:43:41.1045427Z pytest==8.3.5
2025-03-14T04:43:41.1045588Z pytest-benchmark==5.1.0
2025-03-14T04:43:41.1045766Z pytest-cpp==2.3.0
2025-03-14T04:43:41.1045945Z pytest-flakefinder==1.1.0
2025-03-14T04:43:41.1046141Z pytest-rerunfailures==14.0
2025-03-14T04:43:41.1046332Z pytest-subtests==0.13.1
2025-03-14T04:43:41.1046510Z pytest-xdist==3.3.1
2025-03-14T04:43:41.1046686Z python-dateutil==2.9.0.post0
2025-03-14T04:43:41.1046878Z python-doctr==0.10.0
2025-03-14T04:43:41.1047046Z python-etcd==0.4.5
2025-03-14T04:43:41.1047474Z pytorch-labs-segment-anything-fast @ git+https://github.com/pytorch-labs/segment-anything-fast.git@e6aadeb86f3ae1f58c3f98e2a91e251716e0f2aa
2025-03-14T04:43:41.1048127Z -e git+https://github.com/pytorch/pytorch_sphinx_theme.git@4125c834e1aa0945fde6ef58ff2f77f7abedc460#egg=pytorch_sphinx_theme
2025-03-14T04:43:41.1048514Z pytz==2025.1
2025-03-14T04:43:41.1048671Z PyWavelets==1.4.1
2025-03-14T04:43:41.1048881Z PyYAML==6.0.2
2025-03-14T04:43:41.1049040Z pyzmq==26.3.0
2025-03-14T04:43:41.1049185Z pyzstd==0.16.2
2025-03-14T04:43:41.1049343Z RapidFuzz==3.12.2
2025-03-14T04:43:41.1049502Z rdflib==7.1.3
2025-03-14T04:43:41.1049658Z redis==5.2.1
2025-03-14T04:43:41.1049857Z referencing==0.36.2
2025-03-14T04:43:41.1050021Z regex==2024.11.6
2025-03-14T04:43:41.1050185Z requests==2.32.3
2025-03-14T04:43:41.1050358Z requests-oauthlib==2.0.0
2025-03-14T04:43:41.1050542Z resampy==0.4.3
2025-03-14T04:43:41.1050702Z rfc3986==1.5.0
2025-03-14T04:43:41.1050859Z rich==13.9.4
2025-03-14T04:43:41.1051015Z rpds-py==0.23.1
2025-03-14T04:43:41.1051178Z rsa==4.9
2025-03-14T04:43:41.1051326Z s3transfer==0.10.4
2025-03-14T04:43:41.1051621Z safetensors==0.5.3
2025-03-14T04:43:41.1051816Z scikit-image==0.19.3
2025-03-14T04:43:41.1051996Z scikit-learn==1.6.1
2025-03-14T04:43:41.1052180Z scipy==1.10.1
2025-03-14T04:43:41.1052353Z segments==2.3.0
2025-03-14T04:43:41.1052536Z sentencepiece==0.2.0
2025-03-14T04:43:41.1052725Z sentry-sdk==2.22.0
2025-03-14T04:43:41.1052915Z setproctitle==1.3.5
2025-03-14T04:43:41.1053101Z shapely==2.0.7
2025-03-14T04:43:41.1053285Z shellingham==1.5.4
2025-03-14T04:43:41.1053457Z simplejson==3.20.1
2025-03-14T04:43:41.1053620Z six==1.17.0
2025-03-14T04:43:41.1053781Z smart-open==7.1.0
2025-03-14T04:43:41.1053952Z smmap==5.0.2
2025-03-14T04:43:41.1054117Z snowballstemmer==2.2.0
2025-03-14T04:43:41.1054301Z sortedcontainers==2.4.0
2025-03-14T04:43:41.1054480Z soundfile==0.13.1
2025-03-14T04:43:41.1054645Z soupsieve==2.6
2025-03-14T04:43:41.1054809Z soxr==0.5.0.post1
2025-03-14T04:43:41.1054972Z spacy==3.8.3
2025-03-14T04:43:41.1055134Z spacy-legacy==3.0.12
2025-03-14T04:43:41.1055312Z spacy-loggers==1.0.5
2025-03-14T04:43:41.1055480Z Sphinx==5.3.0
2025-03-14T04:43:41.1055650Z sphinx-copybutton==0.5.0
2025-03-14T04:43:41.1055840Z sphinx-panels==0.4.1
2025-03-14T04:43:41.1056037Z sphinxcontrib-applehelp==2.0.0
2025-03-14T04:43:41.1056234Z sphinxcontrib-devhelp==2.0.0
2025-03-14T04:43:41.1056433Z sphinxcontrib-htmlhelp==2.1.0
2025-03-14T04:43:41.1056634Z sphinxcontrib-jsmath==1.0.1
2025-03-14T04:43:41.1056827Z sphinxcontrib-katex==0.8.6
2025-03-14T04:43:41.1057018Z sphinxcontrib-qthelp==2.0.0
2025-03-14T04:43:41.1057231Z sphinxcontrib-serializinghtml==2.0.0
2025-03-14T04:43:41.1057447Z SQLAlchemy==2.0.39
2025-03-14T04:43:41.1057610Z srsly==2.5.1
2025-03-14T04:43:41.1057771Z stack-data==0.6.3
2025-03-14T04:43:41.1057935Z stempeg==0.2.3
2025-03-14T04:43:41.1058099Z submitit==1.5.2
2025-03-14T04:43:41.1058259Z sympy==1.13.3
2025-03-14T04:43:41.1058419Z tabulate==0.9.0
2025-03-14T04:43:41.1058586Z tb-nightly==2.13.0a20230426
2025-03-14T04:43:41.1058772Z tensorboard==2.13.0
2025-03-14T04:43:41.1058950Z tensorboard-data-server==0.7.2
2025-03-14T04:43:41.1059142Z tensorboardX==2.6.2.2
2025-03-14T04:43:41.1059313Z termcolor==2.5.0
2025-03-14T04:43:41.1059473Z thinc==8.3.4
2025-03-14T04:43:41.1059633Z threadpoolctl==3.6.0
2025-03-14T04:43:41.1059799Z thriftpy2==0.5.2
2025-03-14T04:43:41.1059962Z tifffile==2024.8.30
2025-03-14T04:43:41.1060306Z timm @ git+https://github.com/huggingface/pytorch-image-models.git@730b907b4d45a4713cbc425cbf224c46089fd514
2025-03-14T04:43:41.1061907Z tlparse==0.3.30
2025-03-14T04:43:41.1062129Z tokenizers==0.15.2
2025-03-14T04:43:41.1062320Z tomli==2.2.1
2025-03-14T04:43:41.1062486Z tomlkit==0.13.2
2025-03-14T04:43:41.1063008Z torch @ file:///var/lib/jenkins/workspace/dist/torch-2.8.0a0%2Bgitaed0b7a-cp39-cp39-linux_x86_64.whl#sha256=70b47dc351bf5a859f8225ee88934d219f3ed496ecea95a101a28f8ac92d2f63
2025-03-14T04:43:41.1063695Z torch_geometric @ git+https://github.com/pyg-team/pytorch_geometric.git@cabcd4097442ba60aa1efa11e1619dd9bb8fb527
2025-03-14T04:43:41.1064169Z torchao @ git+https://github.com/pytorch/ao.git@9259584f98db0760b27492a63050a2915c753dbe
2025-03-14T04:43:41.1064607Z torchaudio @ git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-14T04:43:41.1065110Z torchmultimodal @ git+https://github.com/facebookresearch/multimodal.git@6569fcc03450c2360b50d772bf9b18ec3487fcf4
2025-03-14T04:43:41.1066558Z torchvision @ git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:43:41.1066897Z tornado==6.4.2
2025-03-14T04:43:41.1067056Z tqdm==4.67.1
2025-03-14T04:43:41.1067291Z traitlets==5.14.3
2025-03-14T04:43:41.1067462Z transformers==4.38.1
2025-03-14T04:43:41.1067634Z treetable==0.2.5
2025-03-14T04:43:41.1067830Z triton @ file:///var/lib/jenkins/triton/python
2025-03-14T04:43:41.1068056Z typeguard==4.4.2
2025-03-14T04:43:41.1068222Z typer==0.15.2
2025-03-14T04:43:41.1068388Z typing-inspect==0.9.0
2025-03-14T04:43:41.1068574Z typing_extensions==4.12.2
2025-03-14T04:43:41.1068756Z tzdata==2025.1
2025-03-14T04:43:41.1068914Z Unidecode==1.3.8
2025-03-14T04:43:41.1069091Z unittest-xml-reporting==3.2.0
2025-03-14T04:43:41.1069284Z uritemplate==4.1.1
2025-03-14T04:43:41.1069448Z urllib3==1.26.20
2025-03-14T04:43:41.1069601Z visdom==0.2.4
2025-03-14T04:43:41.1069757Z wandb==0.19.8
2025-03-14T04:43:41.1069913Z wasabi==1.1.3
2025-03-14T04:43:41.1070076Z wcwidth==0.2.13
2025-03-14T04:43:41.1070238Z weasel==0.4.1
2025-03-14T04:43:41.1070402Z websocket-client==1.8.0
2025-03-14T04:43:41.1070584Z Werkzeug==3.1.3
2025-03-14T04:43:41.1070746Z wrapt==1.17.2
2025-03-14T04:43:41.1070906Z xdoctest==1.1.0
2025-03-14T04:43:41.1071065Z xxhash==3.5.0
2025-03-14T04:43:41.1071235Z xyzservices==2025.1.0
2025-03-14T04:43:41.1071398Z yacs==0.1.8
2025-03-14T04:43:41.1071544Z yarl==1.18.3
2025-03-14T04:43:41.1071688Z z3-solver==4.12.6.0
2025-03-14T04:43:41.1071844Z zipp==3.21.0
2025-03-14T04:43:41.1397035Z + popd
2025-03-14T04:43:41.1397319Z ~/workspace
2025-03-14T04:43:41.1397541Z + [[ dynamic_cpu_inductor_torchbench != *cpu* ]]
2025-03-14T04:43:41.1397792Z ++ pwd
2025-03-14T04:43:41.1398002Z + PYTHONPATH=/var/lib/jenkins/workspace/torchbench
2025-03-14T04:43:41.1398266Z + test_dynamo_benchmark torchbench 0
2025-03-14T04:43:41.1403967Z ++ pwd
2025-03-14T04:43:41.1404423Z + TEST_REPORTS_DIR=/var/lib/jenkins/workspace/test/test-reports
2025-03-14T04:43:41.1404760Z + local suite=torchbench
2025-03-14T04:43:41.1404964Z + shift
2025-03-14T04:43:41.1405127Z + local shard_id=0
2025-03-14T04:43:41.1405297Z + shift
2025-03-14T04:43:41.1405504Z + [[ dynamic_cpu_inductor_torchbench == *perf_compare* ]]
2025-03-14T04:43:41.1405786Z + [[ dynamic_cpu_inductor_torchbench == *perf* ]]
2025-03-14T04:43:41.1406045Z + [[ dynamic_cpu_inductor_torchbench == *cpu* ]]
2025-03-14T04:43:41.1406274Z + local dt=float32
2025-03-14T04:43:41.1406473Z + [[ dynamic_cpu_inductor_torchbench == *amp* ]]
2025-03-14T04:43:41.1406739Z + [[ dynamic_cpu_inductor_torchbench == *freezing* ]]
2025-03-14T04:43:41.1407066Z + test_single_dynamo_benchmark inference torchbench 0 --inference --float32
2025-03-14T04:43:41.1408641Z ++ pwd
2025-03-14T04:43:41.1410389Z + TEST_REPORTS_DIR=/var/lib/jenkins/workspace/test/test-reports
2025-03-14T04:43:41.1410733Z + mkdir -p /var/lib/jenkins/workspace/test/test-reports
2025-03-14T04:43:41.1430159Z + local name=inference
2025-03-14T04:43:41.1430566Z + shift
2025-03-14T04:43:41.1430855Z + local suite=torchbench
2025-03-14T04:43:41.1431235Z + shift
2025-03-14T04:43:41.1431473Z + local shard_id=0
2025-03-14T04:43:41.1431750Z + shift
2025-03-14T04:43:41.1432788Z + partition_flags=()
2025-03-14T04:43:41.1433358Z + local partition_flags
2025-03-14T04:43:41.1433717Z + [[ -n 2 ]]
2025-03-14T04:43:41.1434049Z + [[ -n 0 ]]
2025-03-14T04:43:41.1434443Z + partition_flags=(--total-partitions "$NUM_TEST_SHARDS" --partition-id "$shard_id")
2025-03-14T04:43:41.1434937Z + [[ dynamic_cpu_inductor_torchbench == *perf_compare* ]]
2025-03-14T04:43:41.1435222Z + [[ dynamic_cpu_inductor_torchbench == *perf* ]]
2025-03-14T04:43:41.1435512Z + [[ dynamic_cpu_inductor_torchbench == *_avx2* ]]
2025-03-14T04:43:41.1435800Z + [[ dynamic_cpu_inductor_torchbench == *_avx512* ]]
2025-03-14T04:43:41.1437681Z + python benchmarks/dynamo/torchbench.py --ci --accuracy --timing --explain --print-compilation-time --inductor --dynamic-shapes --dynamic-batch-only --device cpu --inference --float32 --total-partitions 2 --partition-id 0 --output /var/lib/jenkins/workspace/test/test-reports/inference_torchbench.csv
2025-03-14T04:43:45.8889871Z 
2025-03-14T04:43:47.6036369Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:43:47.6039924Z loading model: 0it [00:01, ?it/s]
2025-03-14T04:43:47.6048863Z cpu  eval  BERT_pytorch                       
2025-03-14T04:43:48.1845391Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:43:48.3988929Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:43:48.6229676Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:44:22.7212397Z Compilation time (from dynamo_timed): 32.833837008
2025-03-14T04:44:22.7244936Z pass
2025-03-14T04:44:22.7245369Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:44:22.7246300Z TIMING: _recursive_pre_grad_passes:0.00792 _recursive_joint_graph_passes:0.55176 _recursive_post_grad_passes:0.32438 async_compile.wait:1.30748 code_gen:12.46208 inductor_compile:16.71513 backend_compile:27.91458 entire_frame_compile:32.83384 gc:0.00015 total_wall_time:32.83384
2025-03-14T04:44:22.7247305Z STATS: call_* op count: 543 | FakeTensor.__torch_dispatch__:1438 | FakeTensorMode.__torch_dispatch__:25321 | attempt fast:1494 | fast is_contiguous:1494 | ProxyTorchDispatchMode.__torch_dispatch__:6318
2025-03-14T04:44:22.7247872Z Dynamo produced 1 graphs covering 543 ops with 0 graph breaks (0 unique)
2025-03-14T04:44:28.1298009Z 
2025-03-14T04:44:31.0752088Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:44:31.0756809Z loading model: 0it [00:02, ?it/s]
2025-03-14T04:44:31.0763230Z cpu  eval  Background_Matting                 
2025-03-14T04:44:31.1065585Z Compilation time (from dynamo_timed): 0
2025-03-14T04:44:31.1066714Z pass_due_to_skip
2025-03-14T04:44:31.1067276Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:44:31.1067728Z TIMING: total_wall_time:0
2025-03-14T04:44:31.1068435Z STATS: call_* op count: 0
2025-03-14T04:44:31.1068912Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique)
2025-03-14T04:44:34.2274876Z 
2025-03-14T04:44:36.1046491Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:44:36.1047525Z loading model: 0it [00:01, ?it/s]
2025-03-14T04:44:36.1092136Z cpu  eval  LearningToPaint                    
2025-03-14T04:44:36.2565119Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:44:36.2910332Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:44:36.4532092Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:44:50.5788387Z Compilation time (from dynamo_timed): 13.266598857
2025-03-14T04:44:50.5788690Z pass
2025-03-14T04:44:50.5789682Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:44:50.5790542Z TIMING: _recursive_pre_grad_passes:0.0044 _recursive_joint_graph_passes:0.10685 _recursive_post_grad_passes:0.03399 async_compile.wait:1.37595 code_gen:8.28623 inductor_compile:9.23391 backend_compile:11.53251 entire_frame_compile:13.2666 gc:0.00045 total_wall_time:13.2666
2025-03-14T04:44:50.5791537Z STATS: call_* op count: 71 | FakeTensorMode.__torch_dispatch__:6381 | attempt fast:368 | fast is_contiguous:368 | ProxyTorchDispatchMode.__torch_dispatch__:1906 | FakeTensor.__torch_dispatch__:590
2025-03-14T04:44:50.5792115Z Dynamo produced 1 graphs covering 71 ops with 0 graph breaks (0 unique)
2025-03-14T04:44:54.8675391Z 
2025-03-14T04:44:55.7173514Z loading model: 0it [00:00, ?it/s]Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/vgg16-397923af.pth
2025-03-14T04:44:55.7433342Z 
2025-03-14T04:44:55.7433718Z 
2025-03-14T04:44:55.8437036Z   0% 0.00/528M [00:00<?, ?B/s]
2025-03-14T04:44:55.8437264Z 
2025-03-14T04:44:55.9440348Z  11% 55.9M/528M [00:00<00:00, 585MB/s]
2025-03-14T04:44:55.9441142Z 
2025-03-14T04:44:56.0439665Z  21% 113M/528M [00:00<00:00, 592MB/s] 
2025-03-14T04:44:56.0440371Z 
2025-03-14T04:44:56.1482453Z  32% 170M/528M [00:00<00:00, 594MB/s]
2025-03-14T04:44:56.1482933Z 
2025-03-14T04:44:56.2486237Z  43% 227M/528M [00:00<00:00, 584MB/s]
2025-03-14T04:44:56.2486510Z 
2025-03-14T04:44:56.3484436Z  54% 285M/528M [00:00<00:00, 595MB/s]
2025-03-14T04:44:56.3484649Z 
2025-03-14T04:44:56.4490559Z  65% 342M/528M [00:00<00:00, 596MB/s]
2025-03-14T04:44:56.4490808Z 
2025-03-14T04:44:56.5487367Z  76% 399M/528M [00:00<00:00, 596MB/s]
2025-03-14T04:44:56.5487601Z 
2025-03-14T04:44:56.6488479Z  86% 456M/528M [00:00<00:00, 596MB/s]
2025-03-14T04:44:56.6488726Z 
2025-03-14T04:44:56.6756252Z  97% 513M/528M [00:00<00:00, 596MB/s]
2025-03-14T04:44:56.6756565Z 100% 528M/528M [00:00<00:00, 594MB/s]
2025-03-14T04:44:58.3264358Z 
2025-03-14T04:44:58.3265356Z loading model: 0it [00:03, ?it/s]
2025-03-14T04:44:58.3265770Z cpu  eval  Super_SloMo                        
2025-03-14T04:45:22.2287217Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:45:24.0536502Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:45:25.4815822Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:15.6286498Z Compilation time (from dynamo_timed): 47.684000187
2025-03-14T04:46:15.6292323Z pass
2025-03-14T04:46:15.6296809Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:15.6302056Z TIMING: _recursive_pre_grad_passes:0.00706 _recursive_joint_graph_passes:0.29411 _recursive_post_grad_passes:0.16229 async_compile.wait:2.56929 code_gen:24.55622 inductor_compile:30.87282 backend_compile:42.27602 entire_frame_compile:47.684 gc:0.00033 total_wall_time:47.684
2025-03-14T04:46:15.6304748Z STATS: call_* op count: 374 | FakeTensorMode.__torch_dispatch__:26088 | attempt fast:1606 | fast is_contiguous:1470 | slow no contiguity match:136 | ProxyTorchDispatchMode.__torch_dispatch__:6332 | FakeTensor.__torch_dispatch__:2521
2025-03-14T04:46:15.6305559Z Dynamo produced 1 graphs covering 374 ops with 0 graph breaks (0 unique)
2025-03-14T04:46:21.5004086Z 
2025-03-14T04:46:21.8805459Z loading model: 0it [00:00, ?it/s]Downloading: "https://download.pytorch.org/models/alexnet-owt-7be5be79.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/alexnet-owt-7be5be79.pth
2025-03-14T04:46:21.9057759Z 
2025-03-14T04:46:21.9058093Z 
2025-03-14T04:46:22.0054894Z   0% 0.00/233M [00:00<?, ?B/s]
2025-03-14T04:46:22.0058084Z 
2025-03-14T04:46:22.1112048Z  23% 54.2M/233M [00:00<00:00, 568MB/s]
2025-03-14T04:46:22.1112266Z 
2025-03-14T04:46:22.2109624Z  47% 108M/233M [00:00<00:00, 552MB/s] 
2025-03-14T04:46:22.2109800Z 
2025-03-14T04:46:22.3110311Z  71% 166M/233M [00:00<00:00, 572MB/s]
2025-03-14T04:46:22.3110837Z 
2025-03-14T04:46:22.3308903Z  95% 222M/233M [00:00<00:00, 582MB/s]
2025-03-14T04:46:22.3309499Z 100% 233M/233M [00:00<00:00, 575MB/s]
2025-03-14T04:46:22.6151304Z 
2025-03-14T04:46:22.6154519Z loading model: 0it [00:01, ?it/s]
2025-03-14T04:46:22.6157008Z cpu  eval  alexnet                            
2025-03-14T04:46:22.7562396Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:22.8038757Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:22.8507574Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:34.2188941Z Compilation time (from dynamo_timed): 10.259560097
2025-03-14T04:46:34.2189261Z pass
2025-03-14T04:46:34.2189570Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:34.2190843Z TIMING: _recursive_pre_grad_passes:0.00314 _recursive_joint_graph_passes:0.10228 _recursive_post_grad_passes:0.01504 async_compile.wait:1.32487 code_gen:8.1401 inductor_compile:8.70338 backend_compile:9.92108 entire_frame_compile:10.25956 gc:0.00107 total_wall_time:10.25956
2025-03-14T04:46:34.2191989Z STATS: call_* op count: 22 | FakeTensorMode.__torch_dispatch__:1638 | attempt fast:87 | fast is_contiguous:87 | ProxyTorchDispatchMode.__torch_dispatch__:243 | FakeTensor.__torch_dispatch__:36
2025-03-14T04:46:34.2192535Z Dynamo produced 1 graphs covering 22 ops with 0 graph breaks (0 unique)
2025-03-14T04:46:38.2559000Z 
2025-03-14T04:46:39.8800496Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:46:39.8801219Z loading model: 0it [00:01, ?it/s]
2025-03-14T04:46:39.8801548Z cpu  eval  basic_gnn_edgecnn                  
2025-03-14T04:46:40.5114961Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:41.0196144Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:41.4375268Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:49.2155415Z Compilation time (from dynamo_timed): 7.613465374
2025-03-14T04:46:49.2155738Z pass
2025-03-14T04:46:49.2156045Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:49.2156847Z TIMING: _recursive_pre_grad_passes:0.00389 _recursive_joint_graph_passes:0.10302 _recursive_post_grad_passes:0.01612 async_compile.wait:1.17027 code_gen:6.5085 inductor_compile:6.597 backend_compile:7.31907 entire_frame_compile:7.61347 gc:0.00032 total_wall_time:7.61347
2025-03-14T04:46:49.2157767Z STATS: call_* op count: 55 | FakeTensorMode.__torch_dispatch__:1339 | ProxyTorchDispatchMode.__torch_dispatch__:429 | FakeTensor.__torch_dispatch__:76
2025-03-14T04:46:49.2158308Z Dynamo produced 1 graphs covering 55 ops with 0 graph breaks (0 unique)
2025-03-14T04:46:53.1595776Z 
2025-03-14T04:46:54.7317425Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:46:54.7317913Z loading model: 0it [00:01, ?it/s]
2025-03-14T04:46:54.7318312Z cpu  eval  basic_gnn_gcn                      
2025-03-14T04:46:55.0040325Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:55.1115144Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:55.1933642Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:06.6791309Z Compilation time (from dynamo_timed): 11.201197984
2025-03-14T04:47:06.6791612Z pass
2025-03-14T04:47:06.6791908Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:06.6792727Z TIMING: entire_frame_compile:11.2012 gc:0.00577 _recursive_pre_grad_passes:0.00595 _recursive_joint_graph_passes:0.09951 _recursive_post_grad_passes:0.01689 async_compile.wait:4.58426 code_gen:9.85606 inductor_compile:9.95384 backend_compile:10.70623 total_wall_time:11.2012
2025-03-14T04:47:06.6793605Z STATS: call_* op count: 57 | FakeTensorMode.__torch_dispatch__:1046 | FakeTensor.__torch_dispatch__:59 | ProxyTorchDispatchMode.__torch_dispatch__:220
2025-03-14T04:47:06.6794094Z Dynamo produced 6 graphs covering 57 ops with 6 graph breaks (1 unique)
2025-03-14T04:47:10.9855522Z 
2025-03-14T04:47:12.5750703Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:47:12.5751129Z loading model: 0it [00:01, ?it/s]
2025-03-14T04:47:12.5751449Z cpu  eval  basic_gnn_gin                      
2025-03-14T04:47:12.6768294Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:12.7236254Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:12.7764442Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:20.3709800Z Compilation time (from dynamo_timed): 7.490292139
2025-03-14T04:47:20.3711067Z pass
2025-03-14T04:47:20.3715925Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:20.3717624Z TIMING: _recursive_pre_grad_passes:0.00336 _recursive_joint_graph_passes:0.09925 _recursive_post_grad_passes:0.01536 async_compile.wait:1.1932 code_gen:6.38615 inductor_compile:6.4691 backend_compile:7.19006 entire_frame_compile:7.49029 gc:0.00116 total_wall_time:7.49029
2025-03-14T04:47:20.3718679Z STATS: call_* op count: 52 | FakeTensorMode.__torch_dispatch__:1249 | ProxyTorchDispatchMode.__torch_dispatch__:420 | FakeTensor.__torch_dispatch__:76
2025-03-14T04:47:20.3719250Z Dynamo produced 1 graphs covering 52 ops with 0 graph breaks (0 unique)
2025-03-14T04:47:24.3610878Z 
2025-03-14T04:47:25.9519525Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:47:25.9519941Z loading model: 0it [00:01, ?it/s]
2025-03-14T04:47:25.9521311Z cpu  eval  basic_gnn_sage                     
2025-03-14T04:47:26.0513996Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:26.1007871Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:26.1526887Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:33.6677473Z Compilation time (from dynamo_timed): 7.366192861
2025-03-14T04:47:33.6677841Z pass
2025-03-14T04:47:33.6678182Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:33.6679015Z TIMING: _recursive_pre_grad_passes:0.00335 _recursive_joint_graph_passes:0.10231 _recursive_post_grad_passes:0.02628 async_compile.wait:1.14316 code_gen:6.29926 inductor_compile:6.39962 backend_compile:7.12158 entire_frame_compile:7.36619 gc:0.00043 total_wall_time:7.36619
2025-03-14T04:47:33.6680003Z STATS: call_* op count: 58 | FakeTensorMode.__torch_dispatch__:1273 | ProxyTorchDispatchMode.__torch_dispatch__:444 | FakeTensor.__torch_dispatch__:73
2025-03-14T04:47:33.6680532Z Dynamo produced 1 graphs covering 58 ops with 0 graph breaks (0 unique)
2025-03-14T04:47:37.5694355Z 
2025-03-14T04:47:37.7646992Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:47:37.7647400Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:47:37.7647740Z cpu  eval  dcgan                              
2025-03-14T04:47:37.7800474Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:37.7880984Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:37.7931333Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:47.3016567Z Compilation time (from dynamo_timed): 8.62427754
2025-03-14T04:47:47.3018041Z pass
2025-03-14T04:47:47.3018561Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:47.3019526Z TIMING: _recursive_pre_grad_passes:0.0028 _recursive_joint_graph_passes:0.09132 _recursive_post_grad_passes:0.0168 async_compile.wait:1.25847 code_gen:6.84734 inductor_compile:7.27899 backend_compile:8.24909 entire_frame_compile:8.62428 gc:0.0002 total_wall_time:8.62428
2025-03-14T04:47:47.3020684Z STATS: call_* op count: 13 | FakeTensorMode.__torch_dispatch__:1257 | attempt fast:64 | fast is_contiguous:64 | FakeTensor.__torch_dispatch__:107 | ProxyTorchDispatchMode.__torch_dispatch__:315
2025-03-14T04:47:47.3021343Z Dynamo produced 1 graphs covering 13 ops with 0 graph breaks (0 unique)
2025-03-14T04:47:51.2447259Z 
2025-03-14T04:47:53.3620262Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:47:53.3620866Z loading model: 0it [00:02, ?it/s]
2025-03-14T04:47:53.3626638Z cpu  eval  demucs                             
2025-03-14T04:47:54.9640977Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:55.5098775Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:56.1728182Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:48:10.6857586Z Compilation time (from dynamo_timed): 12.785659977
2025-03-14T04:48:10.6859370Z pass
2025-03-14T04:48:10.6860303Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:48:10.6861409Z TIMING: entire_frame_compile:12.78566 gc:0.00368 _recursive_pre_grad_passes:0.00429 _recursive_joint_graph_passes:0.09624 _recursive_post_grad_passes:0.01828 async_compile.wait:3.74596 code_gen:10.41462 inductor_compile:10.63767 backend_compile:11.53683 total_wall_time:12.78566
2025-03-14T04:48:10.6862373Z STATS: call_* op count: 62 | FakeTensorMode.__torch_dispatch__:2006 | ProxyTorchDispatchMode.__torch_dispatch__:599 | FakeTensor.__torch_dispatch__:87
2025-03-14T04:48:10.6862855Z Dynamo produced 3 graphs covering 62 ops with 3 graph breaks (1 unique)
2025-03-14T04:48:14.7652178Z 
2025-03-14T04:48:14.9752458Z loading model: 0it [00:00, ?it/s]Downloading: "https://download.pytorch.org/models/densenet121-a639ec97.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/densenet121-a639ec97.pth
2025-03-14T04:48:15.0017527Z 
2025-03-14T04:48:15.0020221Z 
2025-03-14T04:48:15.0634802Z   0% 0.00/30.8M [00:00<?, ?B/s]
2025-03-14T04:48:15.0635609Z 100% 30.8M/30.8M [00:00<00:00, 527MB/s]
2025-03-14T04:48:15.5298555Z 
2025-03-14T04:48:15.5299585Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:48:15.5322129Z cpu  eval  densenet121                        
2025-03-14T04:48:16.3028497Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:48:16.5360011Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:48:16.7023356Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:04.5674392Z Compilation time (from dynamo_timed): 46.751492004
2025-03-14T04:49:04.5676889Z pass
2025-03-14T04:49:04.5682709Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:04.5683693Z TIMING: _recursive_pre_grad_passes:0.01211 _recursive_joint_graph_passes:0.20373 _recursive_post_grad_passes:0.17555 async_compile.wait:4.34798 code_gen:17.23797 inductor_compile:23.73964 backend_compile:37.66151 entire_frame_compile:46.75149 gc:0.0015 total_wall_time:46.75149
2025-03-14T04:49:04.5684898Z STATS: call_* op count: 431 | FakeTensorMode.__torch_dispatch__:36107 | attempt fast:1965 | fast is_contiguous:1965 | ProxyTorchDispatchMode.__torch_dispatch__:10672 | FakeTensor.__torch_dispatch__:3450
2025-03-14T04:49:04.5685596Z Dynamo produced 1 graphs covering 431 ops with 0 graph breaks (0 unique)
2025-03-14T04:49:11.0660511Z 
2025-03-14T04:49:24.2097673Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:49:24.2101670Z loading model: 0it [00:13, ?it/s]
2025-03-14T04:49:24.2114532Z cpu  eval  detectron2_fasterrcnn_r_101_c4     
2025-03-14T04:49:36.1048307Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_101_c4. Setting accuracy check to cosine
2025-03-14T04:49:36.1153880Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:55.9856945Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:50:16.7049807Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:50:31.7491676Z 
2025-03-14T04:50:31.7492344Z class GraphModule(torch.nn.Module):
2025-03-14T04:50:31.7635414Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:50:31.7734315Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:50:31.7734841Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7735600Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7736394Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7737273Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7737974Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7738724Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7739464Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7740253Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7741027Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7741770Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7742472Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7743237Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7744072Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7744894Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7745660Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7746347Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7747077Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7747848Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7748589Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7749304Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7750004Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:50:31.7750734Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7751533Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7752353Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:50:31.7753113Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:50:31.7753857Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7754567Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7755303Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7756056Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7756780Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7757469Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7758192Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7758963Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7759715Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7760445Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7761297Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7762015Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7762771Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7763480Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7764175Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7764833Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7765508Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7766283Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7766993Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7767756Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7768449Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7769187Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7770006Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7770837Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7774091Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7774869Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7775598Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7776443Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7777204Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7777953Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7778655Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7779379Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7780161Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7780921Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7781650Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7782345Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7783066Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7783925Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7784757Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7785484Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7786193Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7786957Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7787749Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7788499Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7789226Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7789927Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:50:31.7790669Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7791473Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7792266Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:50:31.7793020Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:50:31.7793707Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7794426Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7795209Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7795967Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7796695Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7797370Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7798082Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7798821Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7799572Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7800272Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7800930Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7801620Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7802360Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7803083Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7803769Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7804421Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7805134Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7805905Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7806690Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7807461Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7808210Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7809004Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7809849Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7810667Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7811550Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7812330Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7813159Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7814034Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7814837Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7815617Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7816364Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7817147Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7817992Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7818787Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7819567Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7820304Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7821078Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7821900Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7822634Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7823317Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7823965Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7824642Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7825395Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7826145Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7826846Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7827528Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7828225Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7829038Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7829782Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7830471Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7831174Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7831857Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7832596Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7833317Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7834009Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7834677Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7835398Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7836176Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7836940Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7837664Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7838372Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:50:31.7839160Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7839971Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7840767Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:50:31.7841568Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:50:31.7842308Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7843153Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7843975Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7844793Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7845565Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7846300Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7847067Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7847884Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7848695Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7849473Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7850224Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7851019Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7852028Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7852869Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7853649Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7854406Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7855195Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7856039Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7856857Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7857687Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7858483Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7859270Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7860109Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7861114Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7861916Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7862713Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7863501Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7864352Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7865192Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7865926Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7866580Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7867260Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7867993Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7868707Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7869402Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7870058Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7870753Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7871484Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7872342Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7873038Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7873766Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7874473Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7875209Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7875972Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7876671Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7877360Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7878047Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7878781Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7879495Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7880183Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7880836Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7881519Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7882251Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7882959Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7883651Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7884299Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7884975Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7885738Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7886452Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7887185Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7887906Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7888676Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7889512Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7890314Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7891096Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7891955Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7892769Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7893626Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7894439Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7895230Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7895981Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7896761Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7897594Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7898424Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7899214Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7899952Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7900732Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7901774Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7902671Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7903520Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7904324Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7905073Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7905984Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7906859Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7907686Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7908476Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7909285Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7910098Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7910950Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7911779Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7912631Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7913435Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7914214Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7914977Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7915699Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7916397Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7917207Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7918072Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7918951Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7919753Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7920498Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7921219Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7922075Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7922913Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7923728Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7924550Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7925336Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7926190Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7927045Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7927965Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7928868Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7929765Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7930721Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7931867Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7947178Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7947994Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7948840Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7949654Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7950480Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7951233Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7951941Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7952685Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7953468Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7954229Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7954954Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7955637Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7956350Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7957121Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7957865Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7958587Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7959268Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7959992Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7960880Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7961646Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7962380Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7963164Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7963897Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7964733Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7965492Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7966234Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7966931Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7967662Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7968446Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7969207Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7969962Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7970702Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7971547Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7972394Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7973196Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7973972Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7974715Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7975482Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7976310Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7977112Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7977889Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7978640Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7979336Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7980106Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7980823Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7981511Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7982165Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7982848Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7983579Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7984289Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7984978Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7985638Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7986325Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7987097Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7987818Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7988524Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7989176Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:50:31.7989861Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7990598Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7991311Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.7992067Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.7992727Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:50:31.7993441Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7994175Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7994886Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.7995584Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.7996255Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:50:31.7996957Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.7997689Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.7998403Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.7999089Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.7999738Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8000414Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8001142Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8001854Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8002547Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8003208Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8003896Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8004631Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8005379Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8006087Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8006789Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8007485Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8008243Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8008999Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8009722Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8010408Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8011125Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8011993Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8012786Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8013538Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8014231Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8014942Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8015710Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8016456Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8017170Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8017849Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8018556Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8019366Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8020101Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8020839Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8021508Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8022204Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8022953Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8023686Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8024393Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8025070Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8025764Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8026516Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8027247Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8027939Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8028597Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8029280Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8030022Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8030747Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8031445Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8032098Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8032784Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8033570Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8034322Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8035027Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8035700Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8036407Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8037173Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8037888Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8038584Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8039247Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8039939Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8040678Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8041407Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8042109Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8042778Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8043478Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8044231Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8044965Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8045688Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8046377Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8047125Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8047903Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8048688Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8049415Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8050106Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8050834Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8051718Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8052533Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8053287Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8053970Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8054692Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8055450Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8056184Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8056884Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8057551Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8058243Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8058995Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8059722Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8060426Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8061319Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8062029Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8062838Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8063553Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8064258Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8064928Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8065636Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8066408Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8067151Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8067866Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8068544Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8069261Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8070030Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8070759Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8071460Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8072128Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8072825Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8073577Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8074306Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8075036Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8075699Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8076414Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8077169Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8077892Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8078583Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8079236Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8079922Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8080654Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8081366Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8082049Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8082713Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8083394Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8084136Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8084863Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8085578Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8086260Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8086979Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8087758Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8088508Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8090197Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8090938Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8091872Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8092713Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8093507Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8094234Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8094930Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8095658Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8096435Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8097195Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8097924Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8098616Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8099335Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8100095Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8100816Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8101505Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8102169Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8102853Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8103589Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8104348Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8105038Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8105804Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:50:31.8106542Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:50:31.8107245Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:50:31.8107977Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:50:31.8108758Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:50:31.8109530Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:50:31.8110275Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:50:31.8110743Z         
2025-03-14T04:50:31.8111139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8111951Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8112556Z         
2025-03-14T04:50:31.8112922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8114730Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8116318Z         
2025-03-14T04:50:31.8116703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:50:31.8117196Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:50:31.8117460Z         
2025-03-14T04:50:31.8117908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:50:31.8118581Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:50:31.8118935Z         
2025-03-14T04:50:31.8119278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8120079Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8120635Z         
2025-03-14T04:50:31.8120999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8122946Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8124708Z         
2025-03-14T04:50:31.8125103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8125616Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:50:31.8125890Z         
2025-03-14T04:50:31.8126245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8127025Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8127594Z         
2025-03-14T04:50:31.8127958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8129968Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8131895Z         
2025-03-14T04:50:31.8132326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8132891Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:50:31.8133183Z         
2025-03-14T04:50:31.8133617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8134405Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8135068Z         
2025-03-14T04:50:31.8135439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8137463Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8139294Z         
2025-03-14T04:50:31.8139649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8140448Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:50:31.8141044Z         
2025-03-14T04:50:31.8141412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8143378Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8145092Z         
2025-03-14T04:50:31.8145456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8145957Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:50:31.8146236Z         
2025-03-14T04:50:31.8146624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8147149Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:50:31.8147431Z         
2025-03-14T04:50:31.8147794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8148571Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8149128Z         
2025-03-14T04:50:31.8149471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8151301Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8152968Z         
2025-03-14T04:50:31.8153349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8153849Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:50:31.8154118Z         
2025-03-14T04:50:31.8154467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8155192Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8155729Z         
2025-03-14T04:50:31.8156075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8157944Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8159660Z         
2025-03-14T04:50:31.8160043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8160637Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:50:31.8160926Z         
2025-03-14T04:50:31.8161295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8162178Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8162761Z         
2025-03-14T04:50:31.8163122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8165136Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8166904Z         
2025-03-14T04:50:31.8167315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8167871Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:50:31.8168157Z         
2025-03-14T04:50:31.8168538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8169065Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:50:31.8169365Z         
2025-03-14T04:50:31.8169744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8170563Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8171173Z         
2025-03-14T04:50:31.8171645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8173739Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8175478Z         
2025-03-14T04:50:31.8175865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8176379Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:50:31.8176639Z         
2025-03-14T04:50:31.8177013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8177749Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8178318Z         
2025-03-14T04:50:31.8178664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8180497Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8182088Z         
2025-03-14T04:50:31.8182442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8182905Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:50:31.8183165Z         
2025-03-14T04:50:31.8183494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8184230Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8184780Z         
2025-03-14T04:50:31.8185125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8186983Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8188619Z         
2025-03-14T04:50:31.8188979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8189464Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:50:31.8189728Z         
2025-03-14T04:50:31.8190087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8190603Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:50:31.8190877Z         
2025-03-14T04:50:31.8191217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8191983Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8192525Z         
2025-03-14T04:50:31.8192871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8194745Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8196403Z         
2025-03-14T04:50:31.8196766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8197255Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:50:31.8197519Z         
2025-03-14T04:50:31.8197854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8198597Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8199148Z         
2025-03-14T04:50:31.8199493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8201359Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8203020Z         
2025-03-14T04:50:31.8203390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8203876Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:50:31.8204141Z         
2025-03-14T04:50:31.8204503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8205260Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8205872Z         
2025-03-14T04:50:31.8206239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8208283Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8210145Z         
2025-03-14T04:50:31.8210517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8211355Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:50:31.8212059Z         
2025-03-14T04:50:31.8212455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8214589Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8216514Z         
2025-03-14T04:50:31.8216918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8217459Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:50:31.8217753Z         
2025-03-14T04:50:31.8218152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8218667Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:50:31.8218948Z         
2025-03-14T04:50:31.8219294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8220106Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8220704Z         
2025-03-14T04:50:31.8221066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8223033Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8224760Z         
2025-03-14T04:50:31.8225144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8225648Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:50:31.8225921Z         
2025-03-14T04:50:31.8226269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8227026Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8227577Z         
2025-03-14T04:50:31.8227927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8229764Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8231423Z         
2025-03-14T04:50:31.8231790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8232271Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:50:31.8232532Z         
2025-03-14T04:50:31.8232852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8233636Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8234188Z         
2025-03-14T04:50:31.8234535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8236431Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8238090Z         
2025-03-14T04:50:31.8238456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8238953Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:50:31.8239235Z         
2025-03-14T04:50:31.8239605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8240113Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:50:31.8240406Z         
2025-03-14T04:50:31.8240761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8241545Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8242127Z         
2025-03-14T04:50:31.8242503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8244479Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8246285Z         
2025-03-14T04:50:31.8246699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8247242Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:50:31.8247540Z         
2025-03-14T04:50:31.8247916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8248785Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8249438Z         
2025-03-14T04:50:31.8249835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8252099Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8253979Z         
2025-03-14T04:50:31.8254369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8254872Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:50:31.8255159Z         
2025-03-14T04:50:31.8255523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8256341Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8256982Z         
2025-03-14T04:50:31.8257367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8259408Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8261476Z         
2025-03-14T04:50:31.8261887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8262431Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:50:31.8262735Z         
2025-03-14T04:50:31.8263143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8263684Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:50:31.8264092Z         
2025-03-14T04:50:31.8264471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8265332Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8265940Z         
2025-03-14T04:50:31.8266328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8268405Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8270146Z         
2025-03-14T04:50:31.8270506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8270975Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:50:31.8271229Z         
2025-03-14T04:50:31.8271553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8272269Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8272802Z         
2025-03-14T04:50:31.8273151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8274956Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8276585Z         
2025-03-14T04:50:31.8276949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8277432Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:50:31.8277693Z         
2025-03-14T04:50:31.8278056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8278796Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8279384Z         
2025-03-14T04:50:31.8279734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8281590Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8283237Z         
2025-03-14T04:50:31.8283588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8284066Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:50:31.8284338Z         
2025-03-14T04:50:31.8284699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8285186Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:50:31.8285451Z         
2025-03-14T04:50:31.8285777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8286498Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8287027Z         
2025-03-14T04:50:31.8287368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8289275Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8290996Z         
2025-03-14T04:50:31.8291378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8291995Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:50:31.8292308Z         
2025-03-14T04:50:31.8292674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8293471Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8294018Z         
2025-03-14T04:50:31.8294366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8296274Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8297912Z         
2025-03-14T04:50:31.8298273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8298745Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:50:31.8299002Z         
2025-03-14T04:50:31.8299341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8300077Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8300626Z         
2025-03-14T04:50:31.8300965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8302831Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8304460Z         
2025-03-14T04:50:31.8304795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8305562Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:50:31.8306119Z         
2025-03-14T04:50:31.8306463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8308388Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8310091Z         
2025-03-14T04:50:31.8310443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8310920Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:50:31.8311171Z         
2025-03-14T04:50:31.8311535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8312020Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:50:31.8312280Z         
2025-03-14T04:50:31.8312618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8313339Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8313868Z         
2025-03-14T04:50:31.8314214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8316062Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8317682Z         
2025-03-14T04:50:31.8318049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8318526Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:50:31.8318779Z         
2025-03-14T04:50:31.8319103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8319863Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8320441Z         
2025-03-14T04:50:31.8320786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8322628Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8324252Z         
2025-03-14T04:50:31.8324623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8325096Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:50:31.8325352Z         
2025-03-14T04:50:31.8325680Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8326454Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8327271Z         
2025-03-14T04:50:31.8327772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8330453Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8332699Z         
2025-03-14T04:50:31.8333217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8333747Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:50:31.8334042Z         
2025-03-14T04:50:31.8334422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8334974Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:50:31.8335251Z         
2025-03-14T04:50:31.8335679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8336621Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8337324Z         
2025-03-14T04:50:31.8337692Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8340076Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8342005Z         
2025-03-14T04:50:31.8342504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8343067Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:50:31.8343375Z         
2025-03-14T04:50:31.8343759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8344625Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8345281Z         
2025-03-14T04:50:31.8345657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8347922Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8349976Z         
2025-03-14T04:50:31.8350405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8350940Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:50:31.8351217Z         
2025-03-14T04:50:31.8351572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8352380Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8352975Z         
2025-03-14T04:50:31.8353338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8355280Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8356991Z         
2025-03-14T04:50:31.8357371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8357871Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:50:31.8358147Z         
2025-03-14T04:50:31.8358532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8359031Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:50:31.8359306Z         
2025-03-14T04:50:31.8359654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8360404Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8361065Z         
2025-03-14T04:50:31.8361431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8363415Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8365139Z         
2025-03-14T04:50:31.8365536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8366062Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:50:31.8366347Z         
2025-03-14T04:50:31.8366774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8367605Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8368252Z         
2025-03-14T04:50:31.8368646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8370757Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8372712Z         
2025-03-14T04:50:31.8373123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8373644Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:50:31.8373916Z         
2025-03-14T04:50:31.8374268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8375047Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8375625Z         
2025-03-14T04:50:31.8375997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8378027Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8379749Z         
2025-03-14T04:50:31.8380133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8380641Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:50:31.8380922Z         
2025-03-14T04:50:31.8381307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8381849Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:50:31.8382126Z         
2025-03-14T04:50:31.8382480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8383291Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8383864Z         
2025-03-14T04:50:31.8384234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8386238Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8388008Z         
2025-03-14T04:50:31.8388402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8388910Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:50:31.8389183Z         
2025-03-14T04:50:31.8389544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8390354Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8390954Z         
2025-03-14T04:50:31.8391328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8393321Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8395100Z         
2025-03-14T04:50:31.8395495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8396010Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:50:31.8396276Z         
2025-03-14T04:50:31.8396663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8397458Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8398097Z         
2025-03-14T04:50:31.8398481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8400438Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8402185Z         
2025-03-14T04:50:31.8402586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8403114Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:50:31.8403404Z         
2025-03-14T04:50:31.8403790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8404290Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:50:31.8404561Z         
2025-03-14T04:50:31.8404909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8405676Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8406266Z         
2025-03-14T04:50:31.8406650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8408716Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8410525Z         
2025-03-14T04:50:31.8410928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8411550Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:50:31.8411858Z         
2025-03-14T04:50:31.8412241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8413111Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8413733Z         
2025-03-14T04:50:31.8414126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8416196Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8418028Z         
2025-03-14T04:50:31.8418444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8418977Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:50:31.8419269Z         
2025-03-14T04:50:31.8419638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8420472Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8421084Z         
2025-03-14T04:50:31.8421474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8423426Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8425070Z         
2025-03-14T04:50:31.8425434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8425915Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:50:31.8426184Z         
2025-03-14T04:50:31.8426586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8427065Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:50:31.8427355Z         
2025-03-14T04:50:31.8427687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8428413Z         x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8428945Z         
2025-03-14T04:50:31.8429288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8431146Z         x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8432785Z         
2025-03-14T04:50:31.8433156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8433634Z         out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:50:31.8433890Z         
2025-03-14T04:50:31.8434220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8434949Z         x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8435510Z         
2025-03-14T04:50:31.8435875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8437778Z         x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8439424Z         
2025-03-14T04:50:31.8439790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8440286Z         out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:50:31.8440536Z         
2025-03-14T04:50:31.8440870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8441626Z         x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8442172Z         
2025-03-14T04:50:31.8442516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8444374Z         x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8446026Z         
2025-03-14T04:50:31.8446404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8446909Z         x_93 += out_51;  out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:50:31.8447185Z         
2025-03-14T04:50:31.8447565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8448069Z         out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:50:31.8448340Z         
2025-03-14T04:50:31.8448684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8449439Z         x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8449999Z         
2025-03-14T04:50:31.8450363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8452519Z         x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8454327Z         
2025-03-14T04:50:31.8454770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8455271Z         out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:50:31.8455569Z         
2025-03-14T04:50:31.8455917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8456686Z         x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8457249Z         
2025-03-14T04:50:31.8457606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8459561Z         x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8461414Z         
2025-03-14T04:50:31.8461811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8462314Z         out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:50:31.8462582Z         
2025-03-14T04:50:31.8462928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8463662Z         x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8464202Z         
2025-03-14T04:50:31.8464553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8466417Z         x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8468060Z         
2025-03-14T04:50:31.8468420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8468959Z         x_99 += out_55;  out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:50:31.8469232Z         
2025-03-14T04:50:31.8469617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8470138Z         out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:50:31.8470390Z         
2025-03-14T04:50:31.8470725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8471452Z         x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8471992Z         
2025-03-14T04:50:31.8472332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8474190Z         x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8475834Z         
2025-03-14T04:50:31.8476197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8476674Z         out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:50:31.8476930Z         
2025-03-14T04:50:31.8477258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8477986Z         x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8478522Z         
2025-03-14T04:50:31.8478868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8480721Z         x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8482353Z         
2025-03-14T04:50:31.8482751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8483225Z         out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:50:31.8483533Z         
2025-03-14T04:50:31.8483868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8484599Z         x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8485144Z         
2025-03-14T04:50:31.8485491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8487350Z         x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8489082Z         
2025-03-14T04:50:31.8489461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8489972Z         x_105 += out_59;  out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:50:31.8490256Z         
2025-03-14T04:50:31.8490647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8491152Z         out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:50:31.8491422Z         
2025-03-14T04:50:31.8491842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8492611Z         x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8493178Z         
2025-03-14T04:50:31.8493543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8495545Z         x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8497278Z         
2025-03-14T04:50:31.8497667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8498209Z         out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:50:31.8498477Z         
2025-03-14T04:50:31.8498829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8499609Z         x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8500180Z         
2025-03-14T04:50:31.8500530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8502372Z         x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8504012Z         
2025-03-14T04:50:31.8504377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8504857Z         out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:50:31.8505125Z         
2025-03-14T04:50:31.8505473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8506239Z         x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8506782Z         
2025-03-14T04:50:31.8507131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8509019Z         x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8510649Z         
2025-03-14T04:50:31.8511038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8511525Z         x_111 += out_63;  out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:50:31.8511824Z         
2025-03-14T04:50:31.8512185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8512662Z         out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:50:31.8512920Z         
2025-03-14T04:50:31.8513252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8513985Z         x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8514533Z         
2025-03-14T04:50:31.8514896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8516565Z         x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8516632Z         
2025-03-14T04:50:31.8516939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8517090Z         out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:50:31.8517154Z         
2025-03-14T04:50:31.8517428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8517884Z         x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8517954Z         
2025-03-14T04:50:31.8518231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8519914Z         x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8519989Z         
2025-03-14T04:50:31.8520293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8520471Z         out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:50:31.8520537Z         
2025-03-14T04:50:31.8520804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8521255Z         x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8521330Z         
2025-03-14T04:50:31.8521604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8523250Z         x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8523327Z         
2025-03-14T04:50:31.8523618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8523784Z         x_117 += out_67;  out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:50:31.8523847Z         
2025-03-14T04:50:31.8524149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8524293Z         out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:50:31.8524362Z         
2025-03-14T04:50:31.8524620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8525072Z         x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8525139Z         
2025-03-14T04:50:31.8525422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8527088Z         x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8527191Z         
2025-03-14T04:50:31.8527503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8527644Z         out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:50:31.8527714Z         
2025-03-14T04:50:31.8527979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8528442Z         x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8528510Z         
2025-03-14T04:50:31.8528816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8530577Z         x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8530654Z         
2025-03-14T04:50:31.8530979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8531126Z         out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:50:31.8531198Z         
2025-03-14T04:50:31.8531549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8532067Z         x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8532137Z         
2025-03-14T04:50:31.8532446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8534292Z         x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8534411Z         
2025-03-14T04:50:31.8534737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8534906Z         x_123 += out_71;  out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:50:31.8534982Z         
2025-03-14T04:50:31.8535305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8535467Z         out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:50:31.8535536Z         
2025-03-14T04:50:31.8535827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8536309Z         x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8536390Z         
2025-03-14T04:50:31.8536688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8538480Z         x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8538560Z         
2025-03-14T04:50:31.8538883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8539038Z         out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:50:31.8539107Z         
2025-03-14T04:50:31.8539396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8539886Z         x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8539966Z         
2025-03-14T04:50:31.8540269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8541829Z         x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8541927Z         
2025-03-14T04:50:31.8542209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8542345Z         out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:50:31.8542406Z         
2025-03-14T04:50:31.8542658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8543082Z         x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8543154Z         
2025-03-14T04:50:31.8543415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8544967Z         x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8545039Z         
2025-03-14T04:50:31.8545313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8545465Z         x_129 += out_75;  out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:50:31.8545524Z         
2025-03-14T04:50:31.8545808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8545943Z         out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:50:31.8546014Z         
2025-03-14T04:50:31.8546261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8546684Z         x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8546746Z         
2025-03-14T04:50:31.8547016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8548574Z         x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8548665Z         
2025-03-14T04:50:31.8548956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8549088Z         out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:50:31.8549157Z         
2025-03-14T04:50:31.8549413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8549863Z         x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8549937Z         
2025-03-14T04:50:31.8550214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8551836Z         x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8551904Z         
2025-03-14T04:50:31.8552208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8552348Z         out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:50:31.8552421Z         
2025-03-14T04:50:31.8552680Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8553135Z         x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8553211Z         
2025-03-14T04:50:31.8553485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8555147Z         x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8555241Z         
2025-03-14T04:50:31.8555542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8555696Z         x_135 += out_79;  out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:50:31.8555767Z         
2025-03-14T04:50:31.8556063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8556214Z         out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:50:31.8556285Z         
2025-03-14T04:50:31.8556546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8556996Z         x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8557060Z         
2025-03-14T04:50:31.8557343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8558968Z         x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8559044Z         
2025-03-14T04:50:31.8559348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8559488Z         out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:50:31.8559560Z         
2025-03-14T04:50:31.8559819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8560280Z         x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8560345Z         
2025-03-14T04:50:31.8560731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8562441Z         x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8562558Z         
2025-03-14T04:50:31.8562863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8563002Z         out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:50:31.8563077Z         
2025-03-14T04:50:31.8563336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8563801Z         x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8563867Z         
2025-03-14T04:50:31.8564148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8565777Z         x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8565853Z         
2025-03-14T04:50:31.8566150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8566304Z         x_141 += out_83;  out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:50:31.8566376Z         
2025-03-14T04:50:31.8566672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8566823Z         out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:50:31.8566891Z         
2025-03-14T04:50:31.8567168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8567627Z         x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8567702Z         
2025-03-14T04:50:31.8567994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8569764Z         x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8569870Z         
2025-03-14T04:50:31.8570191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8570349Z         out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:50:31.8570421Z         
2025-03-14T04:50:31.8570709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8571198Z         x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8571275Z         
2025-03-14T04:50:31.8571668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8573467Z         x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8573555Z         
2025-03-14T04:50:31.8573856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8574002Z         out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:50:31.8574064Z         
2025-03-14T04:50:31.8574334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8574794Z         x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8574871Z         
2025-03-14T04:50:31.8575163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8576944Z         x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8577641Z         
2025-03-14T04:50:31.8577958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8578137Z         x_147 += out_87;  out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:50:31.8578206Z         
2025-03-14T04:50:31.8578528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8578685Z         out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:50:31.8578760Z         
2025-03-14T04:50:31.8579037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8579517Z         x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8579583Z         
2025-03-14T04:50:31.8579878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8581552Z         x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8581618Z         
2025-03-14T04:50:31.8581908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8582043Z         out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:50:31.8582112Z         
2025-03-14T04:50:31.8582360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8582786Z         x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8582847Z         
2025-03-14T04:50:31.8583148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8584700Z         x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8584789Z         
2025-03-14T04:50:31.8585081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8585211Z         out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:50:31.8585283Z         
2025-03-14T04:50:31.8585528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8585954Z         x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8586013Z         
2025-03-14T04:50:31.8586278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8587818Z         x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8587882Z         
2025-03-14T04:50:31.8588168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8588316Z         x_153 += out_91;  out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:50:31.8588387Z         
2025-03-14T04:50:31.8588667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8588809Z         out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:50:31.8588869Z         
2025-03-14T04:50:31.8589121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8589572Z         x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8589641Z         
2025-03-14T04:50:31.8589903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8591493Z         x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8591567Z         
2025-03-14T04:50:31.8591858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8592001Z         out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:50:31.8592064Z         
2025-03-14T04:50:31.8592319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8592752Z         x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8592814Z         
2025-03-14T04:50:31.8593087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8594655Z         x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8594724Z         
2025-03-14T04:50:31.8595007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8595151Z         out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:50:31.8595212Z         
2025-03-14T04:50:31.8595467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8595903Z         x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8595967Z         
2025-03-14T04:50:31.8596289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8597897Z         x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8597999Z         
2025-03-14T04:50:31.8598300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8598458Z         x_159 += out_95;  out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:50:31.8598528Z         
2025-03-14T04:50:31.8598821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8598969Z         out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:50:31.8599033Z         
2025-03-14T04:50:31.8599297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8599737Z         x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8599810Z         
2025-03-14T04:50:31.8600087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8601725Z         x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8601800Z         
2025-03-14T04:50:31.8602094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8602242Z         out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:50:31.8602307Z         
2025-03-14T04:50:31.8602571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8603059Z         x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8603132Z         
2025-03-14T04:50:31.8603408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8605058Z         x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8605132Z         
2025-03-14T04:50:31.8605431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8605579Z         out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:50:31.8605642Z         
2025-03-14T04:50:31.8605911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8606370Z         x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8606444Z         
2025-03-14T04:50:31.8606725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8608355Z         x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8608428Z         
2025-03-14T04:50:31.8608727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8608905Z         x_165 += out_99;  out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:50:31.8608974Z         
2025-03-14T04:50:31.8609298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8609457Z         out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:50:31.8609531Z         
2025-03-14T04:50:31.8609807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8610310Z         x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8610443Z         
2025-03-14T04:50:31.8610742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8612580Z         x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8612659Z         
2025-03-14T04:50:31.8612988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8613128Z         out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:50:31.8613203Z         
2025-03-14T04:50:31.8613465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8613927Z         x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8613994Z         
2025-03-14T04:50:31.8614280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8615926Z         x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8615995Z         
2025-03-14T04:50:31.8616313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8616458Z         out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:50:31.8616528Z         
2025-03-14T04:50:31.8616788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8617280Z         x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8617344Z         
2025-03-14T04:50:31.8617658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8619316Z         x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8619383Z         
2025-03-14T04:50:31.8619682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8619847Z         x_171 += out_103;  out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:50:31.8619922Z         
2025-03-14T04:50:31.8620228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8620379Z         out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:50:31.8620440Z         
2025-03-14T04:50:31.8620697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8621121Z         x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8621191Z         
2025-03-14T04:50:31.8621451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8623032Z         x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8623107Z         
2025-03-14T04:50:31.8623389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8623528Z         out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:50:31.8623588Z         
2025-03-14T04:50:31.8623876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8624304Z         x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8624402Z         
2025-03-14T04:50:31.8624666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8626215Z         x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8626290Z         
2025-03-14T04:50:31.8626590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8626740Z         out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:50:31.8626804Z         
2025-03-14T04:50:31.8627076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8627529Z         x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8627603Z         
2025-03-14T04:50:31.8627889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8629469Z         x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8629542Z         
2025-03-14T04:50:31.8629824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8629992Z         x_177 += out_107;  out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:50:31.8630056Z         
2025-03-14T04:50:31.8630363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8630547Z         out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:50:31.8630614Z         
2025-03-14T04:50:31.8630864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8631314Z         x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8631382Z         
2025-03-14T04:50:31.8631643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8633174Z         x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8633238Z         
2025-03-14T04:50:31.8633523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8633666Z         out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:50:31.8633726Z         
2025-03-14T04:50:31.8633977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8634404Z         x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8634471Z         
2025-03-14T04:50:31.8634731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8636282Z         x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8636353Z         
2025-03-14T04:50:31.8636635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8636776Z         out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:50:31.8636836Z         
2025-03-14T04:50:31.8637122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8637549Z         x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8637653Z         
2025-03-14T04:50:31.8637917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8639469Z         x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8639547Z         
2025-03-14T04:50:31.8639826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8639991Z         x_183 += out_111;  out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:50:31.8640056Z         
2025-03-14T04:50:31.8640345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8640491Z         out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:50:31.8640565Z         
2025-03-14T04:50:31.8640814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8641242Z         x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8641307Z         
2025-03-14T04:50:31.8641580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8643116Z         x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8643194Z         
2025-03-14T04:50:31.8643513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8643648Z         out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:50:31.8643715Z         
2025-03-14T04:50:31.8643991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8644423Z         x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8644486Z         
2025-03-14T04:50:31.8644766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8646379Z         x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8646454Z         
2025-03-14T04:50:31.8646759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8646898Z         out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:50:31.8646968Z         
2025-03-14T04:50:31.8647227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8647703Z         x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8647769Z         
2025-03-14T04:50:31.8648068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8649783Z         x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8649853Z         
2025-03-14T04:50:31.8650168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8650365Z         x_189 += out_115;  out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:50:31.8650440Z         
2025-03-14T04:50:31.8650749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8650943Z         out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:50:31.8651011Z         
2025-03-14T04:50:31.8651583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:50:31.8651765Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:50:31.8651844Z         
2025-03-14T04:50:31.8652187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:31.8652348Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:50:31.8652417Z         
2025-03-14T04:50:31.8652918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:50:31.8653078Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:50:31.8653152Z         
2025-03-14T04:50:31.8653462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:31.8653613Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:50:31.8653678Z         
2025-03-14T04:50:31.8654078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:50:31.8654266Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:50:31.8654380Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:50:31.8654504Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:50:31.8654577Z         
2025-03-14T04:50:31.8654923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:50:31.8655064Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:50:31.8655128Z         
2025-03-14T04:50:31.8655477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:50:31.8655604Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:50:31.8655670Z         
2025-03-14T04:50:31.8656077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:50:31.8656301Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:50:31.8656373Z         
2025-03-14T04:50:31.8656849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:50:31.8656985Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:50:31.8657464Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:50:31.8657600Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:50:31.8657748Z         x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:50:31.8657820Z         
2025-03-14T04:50:31.8658130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:50:31.8658266Z         tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32);  x_190 = None
2025-03-14T04:50:31.8658331Z         
2025-03-14T04:50:31.8658605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8659432Z         x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:50:31.8659509Z         
2025-03-14T04:50:31.8659791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:50:31.8659996Z         x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False);  x_191 = None
2025-03-14T04:50:31.8660060Z         
2025-03-14T04:50:31.8660468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:50:31.8661516Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:50:31.8661588Z         
2025-03-14T04:50:31.8661980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:50:31.8662793Z         x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:50:31.8662866Z         
2025-03-14T04:50:31.8663213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:50:31.8663373Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:50:31.8663517Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:50:31.8663581Z         
2025-03-14T04:50:31.8664083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:50:31.8664241Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75);  x_193 = None
2025-03-14T04:50:31.8664419Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:50:31.8664638Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:50:31.8664709Z         
2025-03-14T04:50:31.8665108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:50:31.8665317Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:50:31.8665379Z         
2025-03-14T04:50:31.8665817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:50:31.8665962Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:50:31.8666115Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:50:31.8666251Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:50:31.8666317Z         
2025-03-14T04:50:31.8666691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:50:31.8666863Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:50:31.8666925Z         
2025-03-14T04:50:31.8667246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:50:31.8667383Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:50:31.8667454Z         
2025-03-14T04:50:31.8667766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:50:31.8667901Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:50:31.8668024Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:50:31.8668174Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:50:31.8668237Z         
2025-03-14T04:50:31.8668560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:50:31.8668679Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:50:31.8668803Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:50:31.8668948Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:50:31.8669019Z         
2025-03-14T04:50:31.8669325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:50:31.8669449Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:50:31.8669535Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:50:31.8669663Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:50:31.8669724Z         
2025-03-14T04:50:31.8670070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:50:31.8670215Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:50:31.8670338Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:50:31.8670464Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:50:31.8670534Z         
2025-03-14T04:50:31.8670883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:50:31.8671042Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:50:31.8671161Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:50:31.8671223Z         
2025-03-14T04:50:31.8671532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:50:31.8671680Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:50:31.8671798Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:50:31.8671860Z         
2025-03-14T04:50:31.8672157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:50:31.8672306Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:50:31.8672429Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:50:31.8672487Z         
2025-03-14T04:50:31.8672785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:50:31.8672964Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:50:31.8673077Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:50:31.8673139Z         
2025-03-14T04:50:31.8673481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:50:31.8673618Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:50:31.8673685Z         
2025-03-14T04:50:31.8674012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:50:31.8674150Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:50:31.8674210Z         
2025-03-14T04:50:31.8674558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:50:31.8674695Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:50:31.8674824Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:50:31.8674971Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:50:31.8675116Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:50:31.8675179Z         
2025-03-14T04:50:31.8675533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:50:31.8675695Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:50:31.8675824Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:50:31.8675971Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:50:31.8676141Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:50:31.8676205Z         
2025-03-14T04:50:31.8676543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:50:31.8676657Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:50:31.8676821Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:50:31.8676955Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:50:31.8677015Z         
2025-03-14T04:50:31.8677353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:50:31.8677466Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:50:31.8677638Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:50:31.8677766Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:50:31.8677838Z         
2025-03-14T04:50:31.8678147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:50:31.8678248Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:50:31.8678362Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:50:31.8678429Z         
2025-03-14T04:50:31.8678735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:50:31.8678827Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:50:31.8678940Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:50:31.8679010Z         
2025-03-14T04:50:31.8679309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:50:31.8679427Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:50:31.8679549Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:50:31.8679616Z         
2025-03-14T04:50:31.8679921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:50:31.8680036Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:50:31.8680157Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:50:31.8680227Z         
2025-03-14T04:50:31.8680570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:50:31.8680755Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:50:31.8680816Z         
2025-03-14T04:50:31.8681155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:50:31.8681312Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:50:31.8681407Z         
2025-03-14T04:50:31.8681786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:50:31.8681991Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:50:31.8682053Z         
2025-03-14T04:50:31.8682542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:50:31.8682673Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:50:31.8682741Z         
2025-03-14T04:50:31.8683035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:31.8683180Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:50:31.8683240Z         
2025-03-14T04:50:31.8683678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:50:31.8683790Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:50:31.8683900Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:50:31.8684011Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:50:31.8684079Z         
2025-03-14T04:50:31.8684539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:50:31.8684709Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:50:31.8684947Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:50:31.8685010Z         
2025-03-14T04:50:31.8685470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:50:31.8685631Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:50:31.8685698Z         
2025-03-14T04:50:31.8685989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:31.8686144Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:50:31.8686204Z         
2025-03-14T04:50:31.8686586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:50:31.8686732Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:50:31.8686798Z         
2025-03-14T04:50:31.8687088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:50:31.8687236Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:50:31.8687297Z         
2025-03-14T04:50:31.8687674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:50:31.8687845Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:50:31.8687912Z         
2025-03-14T04:50:31.8688396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:50:31.8688565Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:50:31.8688680Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:50:31.8688838Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:50:31.8688964Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:50:31.8689034Z         
2025-03-14T04:50:31.8689400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:50:31.8689522Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:50:31.8689587Z         
2025-03-14T04:50:31.8690137Z 
2025-03-14T04:50:31.8690239Z class GraphModule(torch.nn.Module):
2025-03-14T04:50:31.8788636Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:50:31.8789570Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:50:31.8789918Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8790323Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8790706Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8791071Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8791430Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8791795Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8792214Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8792614Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8793029Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8793400Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8793757Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8794161Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8794570Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8794957Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8795312Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8795698Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8796106Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8796530Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8796918Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8797304Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8797676Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:50:31.8798116Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8798538Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8798943Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:50:31.8799335Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:50:31.8799652Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8800062Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8800453Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8800859Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8801223Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8801542Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8801958Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8802347Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8802736Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8803097Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8803465Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8803872Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8804322Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8804711Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8805078Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8805399Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8805812Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8806191Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8806551Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8806915Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8807235Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8807633Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8808013Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8808385Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8808746Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8809062Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8809460Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8809844Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8810214Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8810598Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8810925Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8811349Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8811778Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8812155Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8812554Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8812933Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8813324Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8813710Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8814074Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8814434Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8814751Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8815146Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8815597Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8815955Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8816321Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8816654Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:50:31.8817057Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8817445Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8817860Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:50:31.8818228Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:50:31.8818582Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8818968Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8819351Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8819715Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8820062Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8820385Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8820762Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8821151Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8821507Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8821860Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8822188Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8822565Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8822949Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8823310Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8823651Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8823960Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8824336Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8824696Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8825075Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8825421Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8825764Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8826156Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8826541Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8826895Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8827228Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8827546Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8827915Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8828283Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8828638Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8828985Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8829307Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8829686Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8830069Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8830422Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8830777Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8831094Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8831480Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8831903Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8832264Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8832648Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8832962Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8833345Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8833723Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8834087Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8834431Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8834753Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8835137Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8835521Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8835872Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8836208Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8836521Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8836885Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8837262Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8837605Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8837950Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8838260Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8838625Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8839039Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8839387Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8839763Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8840089Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:50:31.8840479Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8840868Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8841240Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:50:31.8841601Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:50:31.8841906Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8842284Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8842649Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8843003Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8843351Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8843663Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8844032Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8844405Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8844769Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8845122Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8845446Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8845856Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8846242Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8846629Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8846995Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8847319Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8847700Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8848082Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8848443Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8848797Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8849111Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8849500Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8849873Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8850239Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8850589Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8850907Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8851306Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8851812Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8852251Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8852655Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8852986Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8853400Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8854141Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8854508Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8854854Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8855182Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8855562Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8855949Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8856305Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8856663Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8856980Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8857368Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8857755Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8858114Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8858475Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8858793Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8859180Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8859554Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8859916Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8860261Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8860763Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8861161Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8861597Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8861945Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8862272Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8862583Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8862941Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8863280Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8863590Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8863906Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8864215Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8864551Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8864886Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8865198Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8865527Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8865829Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8866192Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8866550Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8866893Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8867259Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8867557Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8867935Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8868270Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8868596Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8868907Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8869198Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8869536Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8869877Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8870197Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8870507Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8870797Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8871140Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8871482Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8871801Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8872123Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8872406Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8872755Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8873096Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8873418Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8873785Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8874071Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8874447Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8874781Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8875108Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8875419Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8875712Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8876060Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8876395Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8876720Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8877036Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8877353Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8877710Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8878072Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8878415Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8878749Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8879058Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8879417Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8879789Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8880158Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8880497Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8880842Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8881210Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8881582Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8881927Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8882260Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8882562Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8882925Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8883292Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8883642Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8883972Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8884282Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8884637Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8884996Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8885342Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8885667Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8885975Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8886330Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8886703Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8887082Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8887420Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8887755Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8888125Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8888500Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8888841Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8889179Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8889489Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8889860Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8890218Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8890571Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8890905Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8891227Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8891710Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8892110Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8892501Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8892851Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8893175Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8893551Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8893947Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8894289Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8894659Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8894969Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8895333Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8895694Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8896032Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8896373Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8896673Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8897042Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8897380Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8897705Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8898022Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8898309Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8898656Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8898991Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8899321Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8899635Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8899925Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8900292Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8900640Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8900995Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8901307Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8901597Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8901938Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8902281Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8902607Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8902926Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8903212Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8903558Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8903900Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8904222Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8904537Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8904824Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8905172Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8905507Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8905839Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8906146Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8906439Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8906814Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8907151Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8907518Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8907830Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8908125Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8908467Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8908818Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8909138Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8909455Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8909748Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8910091Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8910441Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8910758Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8911075Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8911363Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8911712Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8912052Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8912379Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8912696Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8913013Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8913364Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8913744Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8914097Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8914421Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8914726Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8915064Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8915412Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8915736Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8916046Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8916347Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8916689Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8917029Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8917347Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8917665Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8917947Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8918293Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8918640Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8918957Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8919299Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8919590Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8919973Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8920315Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8920652Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8920973Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8921267Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8921629Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8921977Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8922313Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8922629Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8922928Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8923277Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8923627Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8923954Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8924272Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8924570Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8924917Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8925269Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8925672Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8926048Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8926385Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8926815Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8927199Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8927581Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8927963Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8928279Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8928690Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8929087Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8929472Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8929811Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8930126Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8930501Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8930875Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8931233Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8931684Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8932046Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8932447Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8932926Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8933318Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8933659Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8933995Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8934375Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8934750Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8935101Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8935443Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8935757Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8936133Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8936500Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8936849Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8937184Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8937505Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8937886Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8938255Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8938602Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8938931Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8939236Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8939607Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8940012Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8940364Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8940737Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8941030Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8941367Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8941711Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8942029Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8942346Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8942630Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8942976Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8943313Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8943641Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8943956Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8944242Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8944591Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8944924Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8945249Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8945558Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8945850Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8946216Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8946563Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8946913Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8947223Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8947517Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8947860Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8948205Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8948529Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8948845Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8949139Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8949481Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8949821Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8950145Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8950459Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8950745Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:50:31.8951095Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8951430Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8951761Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.8952074Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.8952354Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:50:31.8952725Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8953091Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8953416Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.8953728Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.8954022Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:50:31.8954365Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.8954710Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.8955035Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.8955341Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.8955698Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:50:31.8956015Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:50:31.8956336Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:50:31.8956706Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:50:31.8957071Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:50:31.8957421Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:50:31.8957769Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:50:31.8957843Z         
2025-03-14T04:50:31.8958140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8958609Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8958672Z         
2025-03-14T04:50:31.8958989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8960458Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8960673Z         
2025-03-14T04:50:31.8960963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:50:31.8961112Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:50:31.8961187Z         
2025-03-14T04:50:31.8961549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:50:31.8961794Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:50:31.8961858Z         
2025-03-14T04:50:31.8962121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8962548Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8962619Z         
2025-03-14T04:50:31.8962891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8964450Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8964524Z         
2025-03-14T04:50:31.8964814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8964961Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:50:31.8965021Z         
2025-03-14T04:50:31.8965281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8965799Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8965876Z         
2025-03-14T04:50:31.8966155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8967854Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8967929Z         
2025-03-14T04:50:31.8968229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8968386Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:50:31.8968451Z         
2025-03-14T04:50:31.8968723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8969213Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8969294Z         
2025-03-14T04:50:31.8969587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8971275Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8971353Z         
2025-03-14T04:50:31.8971678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8972427Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:50:31.8972516Z         
2025-03-14T04:50:31.8972819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8974451Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8974579Z         
2025-03-14T04:50:31.8974860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8975005Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:50:31.8975073Z         
2025-03-14T04:50:31.8975349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8975504Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:50:31.8975564Z         
2025-03-14T04:50:31.8975818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8976230Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8976298Z         
2025-03-14T04:50:31.8976557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8978065Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8978139Z         
2025-03-14T04:50:31.8978418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8978563Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:50:31.8978626Z         
2025-03-14T04:50:31.8978877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8979304Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8979374Z         
2025-03-14T04:50:31.8979642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8981196Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8981303Z         
2025-03-14T04:50:31.8981591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8981736Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:50:31.8981802Z         
2025-03-14T04:50:31.8982059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8982487Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8982558Z         
2025-03-14T04:50:31.8982819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8984359Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8984431Z         
2025-03-14T04:50:31.8984709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8984867Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:50:31.8984927Z         
2025-03-14T04:50:31.8985215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8985359Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:50:31.8985428Z         
2025-03-14T04:50:31.8985675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8986100Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8986161Z         
2025-03-14T04:50:31.8986460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8988007Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8988092Z         
2025-03-14T04:50:31.8988383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8988522Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:50:31.8988590Z         
2025-03-14T04:50:31.8988837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8989266Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8989327Z         
2025-03-14T04:50:31.8989600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8991153Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8991219Z         
2025-03-14T04:50:31.8991512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8991647Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:50:31.8991718Z         
2025-03-14T04:50:31.8991964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8992402Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.8992466Z         
2025-03-14T04:50:31.8992736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8994310Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8994408Z         
2025-03-14T04:50:31.8994698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.8994851Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:50:31.8994925Z         
2025-03-14T04:50:31.8995207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8995367Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:50:31.8995428Z         
2025-03-14T04:50:31.8995686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8996125Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.8996186Z         
2025-03-14T04:50:31.8996454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.8998017Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.8998088Z         
2025-03-14T04:50:31.8998370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.8998524Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:50:31.8998584Z         
2025-03-14T04:50:31.8998839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.8999288Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.8999377Z         
2025-03-14T04:50:31.8999646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9001214Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9001283Z         
2025-03-14T04:50:31.9001563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9001713Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:50:31.9001773Z         
2025-03-14T04:50:31.9002027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9002465Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9002527Z         
2025-03-14T04:50:31.9002797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9004334Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9004407Z         
2025-03-14T04:50:31.9004665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9005114Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:50:31.9005185Z         
2025-03-14T04:50:31.9005444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9007125Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9007233Z         
2025-03-14T04:50:31.9007526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9007685Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:50:31.9007752Z         
2025-03-14T04:50:31.9008064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9008225Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:50:31.9008304Z         
2025-03-14T04:50:31.9008567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9009060Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9009135Z         
2025-03-14T04:50:31.9009442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9011132Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9011217Z         
2025-03-14T04:50:31.9011601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9011762Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:50:31.9011838Z         
2025-03-14T04:50:31.9012113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9012599Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9012666Z         
2025-03-14T04:50:31.9012968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9014658Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9014766Z         
2025-03-14T04:50:31.9015086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9015231Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:50:31.9015302Z         
2025-03-14T04:50:31.9015564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9016021Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9016085Z         
2025-03-14T04:50:31.9016367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9017990Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9018063Z         
2025-03-14T04:50:31.9018360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9018524Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:50:31.9018594Z         
2025-03-14T04:50:31.9018887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9019049Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:50:31.9019112Z         
2025-03-14T04:50:31.9019379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9019823Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9019897Z         
2025-03-14T04:50:31.9020202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9021848Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9021951Z         
2025-03-14T04:50:31.9022257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9022417Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:50:31.9022486Z         
2025-03-14T04:50:31.9022758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9023222Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9023298Z         
2025-03-14T04:50:31.9023581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9025147Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9025221Z         
2025-03-14T04:50:31.9025512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9025662Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:50:31.9025730Z         
2025-03-14T04:50:31.9025987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9026423Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9026495Z         
2025-03-14T04:50:31.9026763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9028361Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9028465Z         
2025-03-14T04:50:31.9028743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9028903Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:50:31.9028982Z         
2025-03-14T04:50:31.9029273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9029419Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:50:31.9029488Z         
2025-03-14T04:50:31.9029734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9030168Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9030229Z         
2025-03-14T04:50:31.9030498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9032056Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9032119Z         
2025-03-14T04:50:31.9032408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9032548Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:50:31.9032615Z         
2025-03-14T04:50:31.9032860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9033298Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9033395Z         
2025-03-14T04:50:31.9033667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9035211Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9035298Z         
2025-03-14T04:50:31.9035587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9035724Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:50:31.9035793Z         
2025-03-14T04:50:31.9036038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9036472Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9036540Z         
2025-03-14T04:50:31.9036804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9038332Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9038398Z         
2025-03-14T04:50:31.9038679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9038848Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:50:31.9038917Z         
2025-03-14T04:50:31.9039195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9039346Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:50:31.9039408Z         
2025-03-14T04:50:31.9039672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9040120Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9040183Z         
2025-03-14T04:50:31.9040482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9042007Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9042079Z         
2025-03-14T04:50:31.9042358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9042497Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:50:31.9042558Z         
2025-03-14T04:50:31.9042812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9043238Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9043301Z         
2025-03-14T04:50:31.9043568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9045117Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9045185Z         
2025-03-14T04:50:31.9045475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9045606Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:50:31.9045674Z         
2025-03-14T04:50:31.9045916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9046343Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9046432Z         
2025-03-14T04:50:31.9046700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9048256Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9048324Z         
2025-03-14T04:50:31.9048581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9049017Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:50:31.9049088Z         
2025-03-14T04:50:31.9049350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9051033Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9051109Z         
2025-03-14T04:50:31.9051406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9051624Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:50:31.9051698Z         
2025-03-14T04:50:31.9052023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9052180Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:50:31.9052253Z         
2025-03-14T04:50:31.9052532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9052992Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9053056Z         
2025-03-14T04:50:31.9053382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9055005Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9055108Z         
2025-03-14T04:50:31.9055416Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9055556Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:50:31.9055629Z         
2025-03-14T04:50:31.9055889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9056341Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9056403Z         
2025-03-14T04:50:31.9056690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9058297Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9058372Z         
2025-03-14T04:50:31.9058680Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9058818Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:50:31.9058890Z         
2025-03-14T04:50:31.9059153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9059607Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9059671Z         
2025-03-14T04:50:31.9059954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9061666Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9061778Z         
2025-03-14T04:50:31.9062061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9062209Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:50:31.9062278Z         
2025-03-14T04:50:31.9062558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9062706Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:50:31.9062769Z         
2025-03-14T04:50:31.9063020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9063429Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9063500Z         
2025-03-14T04:50:31.9063761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9065294Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9065363Z         
2025-03-14T04:50:31.9065639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9065779Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:50:31.9065839Z         
2025-03-14T04:50:31.9066089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9066504Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9066570Z         
2025-03-14T04:50:31.9066857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9068386Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9068485Z         
2025-03-14T04:50:31.9068768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9068904Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:50:31.9068965Z         
2025-03-14T04:50:31.9069218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9069636Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9069705Z         
2025-03-14T04:50:31.9069964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9071490Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9071562Z         
2025-03-14T04:50:31.9071841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9071991Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:50:31.9072054Z         
2025-03-14T04:50:31.9072336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9072473Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:50:31.9072541Z         
2025-03-14T04:50:31.9072785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9073229Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9073291Z         
2025-03-14T04:50:31.9073562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9075125Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9075187Z         
2025-03-14T04:50:31.9075477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9075609Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:50:31.9075677Z         
2025-03-14T04:50:31.9075926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9076352Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9076414Z         
2025-03-14T04:50:31.9076683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9078209Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9078273Z         
2025-03-14T04:50:31.9078564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9078697Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:50:31.9078772Z         
2025-03-14T04:50:31.9079019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9079448Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9079511Z         
2025-03-14T04:50:31.9079814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9081344Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9081464Z         
2025-03-14T04:50:31.9081750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9081893Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:50:31.9081964Z         
2025-03-14T04:50:31.9082248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9082395Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:50:31.9082456Z         
2025-03-14T04:50:31.9082705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9083115Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9083183Z         
2025-03-14T04:50:31.9083451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9085018Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9085088Z         
2025-03-14T04:50:31.9085372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9085512Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:50:31.9085574Z         
2025-03-14T04:50:31.9085830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9086281Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9086344Z         
2025-03-14T04:50:31.9086632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9088295Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9088369Z         
2025-03-14T04:50:31.9088663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9088807Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:50:31.9088871Z         
2025-03-14T04:50:31.9089139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9089603Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9089667Z         
2025-03-14T04:50:31.9089951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9091617Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9091699Z         
2025-03-14T04:50:31.9091995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9092156Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:50:31.9092225Z         
2025-03-14T04:50:31.9092547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9092705Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:50:31.9092774Z         
2025-03-14T04:50:31.9093059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9093564Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9093667Z         
2025-03-14T04:50:31.9093945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9095572Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9095649Z         
2025-03-14T04:50:31.9095944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9096083Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:50:31.9096147Z         
2025-03-14T04:50:31.9096408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9096859Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9096930Z         
2025-03-14T04:50:31.9097206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9098817Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9098891Z         
2025-03-14T04:50:31.9099186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9099326Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:50:31.9099390Z         
2025-03-14T04:50:31.9099654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9100119Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9100192Z         
2025-03-14T04:50:31.9100473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9102076Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9102144Z         
2025-03-14T04:50:31.9102416Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9102566Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:50:31.9102629Z         
2025-03-14T04:50:31.9102916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9103050Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:50:31.9103118Z         
2025-03-14T04:50:31.9103361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9103775Z         x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9103839Z         
2025-03-14T04:50:31.9104105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9105622Z         x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9105693Z         
2025-03-14T04:50:31.9105979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9106107Z         out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:50:31.9106176Z         
2025-03-14T04:50:31.9106422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9106876Z         x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9106973Z         
2025-03-14T04:50:31.9107243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9108751Z         x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9108823Z         
2025-03-14T04:50:31.9109111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9109238Z         out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:50:31.9109305Z         
2025-03-14T04:50:31.9109549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9109973Z         x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9110035Z         
2025-03-14T04:50:31.9110300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9111829Z         x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9111892Z         
2025-03-14T04:50:31.9112176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9112316Z         x_93 += out_51;  out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:50:31.9112384Z         
2025-03-14T04:50:31.9112661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9112805Z         out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:50:31.9112865Z         
2025-03-14T04:50:31.9113148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9113563Z         x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9113658Z         
2025-03-14T04:50:31.9113927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9115505Z         x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9115578Z         
2025-03-14T04:50:31.9115874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9116012Z         out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:50:31.9116072Z         
2025-03-14T04:50:31.9116329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9116745Z         x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9116818Z         
2025-03-14T04:50:31.9117079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9118600Z         x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9118696Z         
2025-03-14T04:50:31.9118976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9119111Z         out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:50:31.9119172Z         
2025-03-14T04:50:31.9119426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9119873Z         x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9119976Z         
2025-03-14T04:50:31.9120234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9121786Z         x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9121858Z         
2025-03-14T04:50:31.9122132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9122279Z         x_99 += out_55;  out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:50:31.9122339Z         
2025-03-14T04:50:31.9122623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9122758Z         out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:50:31.9122825Z         
2025-03-14T04:50:31.9123070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9123494Z         x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9123556Z         
2025-03-14T04:50:31.9123818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9125363Z         x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9125427Z         
2025-03-14T04:50:31.9125709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9125840Z         out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:50:31.9125907Z         
2025-03-14T04:50:31.9126180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9126608Z         x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9126696Z         
2025-03-14T04:50:31.9126967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9128514Z         x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9128581Z         
2025-03-14T04:50:31.9128871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9129008Z         out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:50:31.9129080Z         
2025-03-14T04:50:31.9129325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9129769Z         x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9129843Z         
2025-03-14T04:50:31.9130106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9131786Z         x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9131865Z         
2025-03-14T04:50:31.9132184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9132352Z         x_105 += out_59;  out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:50:31.9132427Z         
2025-03-14T04:50:31.9132735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9132934Z         out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:50:31.9132996Z         
2025-03-14T04:50:31.9133256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9133761Z         x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9133829Z         
2025-03-14T04:50:31.9134132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9135817Z         x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9135897Z         
2025-03-14T04:50:31.9136215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9136370Z         out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:50:31.9136437Z         
2025-03-14T04:50:31.9136721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9137197Z         x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9137267Z         
2025-03-14T04:50:31.9137564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9139249Z         x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9139328Z         
2025-03-14T04:50:31.9139646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9139776Z         out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:50:31.9139841Z         
2025-03-14T04:50:31.9140127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9140564Z         x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9140655Z         
2025-03-14T04:50:31.9140927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9142473Z         x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9142547Z         
2025-03-14T04:50:31.9142832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9142983Z         x_111 += out_63;  out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:50:31.9143052Z         
2025-03-14T04:50:31.9143334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9143480Z         out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:50:31.9143543Z         
2025-03-14T04:50:31.9143806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9144229Z         x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9144302Z         
2025-03-14T04:50:31.9144567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9146123Z         x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9146199Z         
2025-03-14T04:50:31.9146486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9146687Z         out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:50:31.9146750Z         
2025-03-14T04:50:31.9147014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9147478Z         x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9147545Z         
2025-03-14T04:50:31.9147809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9149359Z         x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9149431Z         
2025-03-14T04:50:31.9149718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9149857Z         out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:50:31.9149920Z         
2025-03-14T04:50:31.9150177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9150609Z         x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9150680Z         
2025-03-14T04:50:31.9150940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9152501Z         x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9152572Z         
2025-03-14T04:50:31.9152851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9153007Z         x_117 += out_67;  out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:50:31.9153068Z         
2025-03-14T04:50:31.9153383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9153522Z         out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:50:31.9153619Z         
2025-03-14T04:50:31.9153867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9154289Z         x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9154348Z         
2025-03-14T04:50:31.9154614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9156152Z         x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9156226Z         
2025-03-14T04:50:31.9156511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9156638Z         out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:50:31.9156706Z         
2025-03-14T04:50:31.9156944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9157354Z         x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9157415Z         
2025-03-14T04:50:31.9157675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9159171Z         x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9159234Z         
2025-03-14T04:50:31.9159552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9159680Z         out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:50:31.9159747Z         
2025-03-14T04:50:31.9159988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9160433Z         x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9160493Z         
2025-03-14T04:50:31.9160845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9162352Z         x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9162416Z         
2025-03-14T04:50:31.9162694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9162845Z         x_123 += out_71;  out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:50:31.9162914Z         
2025-03-14T04:50:31.9163194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9163350Z         out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:50:31.9163409Z         
2025-03-14T04:50:31.9163661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9164065Z         x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9164135Z         
2025-03-14T04:50:31.9164390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9165938Z         x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9166012Z         
2025-03-14T04:50:31.9166291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9166466Z         out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:50:31.9166524Z         
2025-03-14T04:50:31.9166770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9167179Z         x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9167251Z         
2025-03-14T04:50:31.9167521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9169058Z         x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9169130Z         
2025-03-14T04:50:31.9169410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9169545Z         out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:50:31.9169607Z         
2025-03-14T04:50:31.9169872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9170325Z         x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9170388Z         
2025-03-14T04:50:31.9170668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9172362Z         x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9172446Z         
2025-03-14T04:50:31.9172789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9172941Z         x_129 += out_75;  out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:50:31.9173032Z         
2025-03-14T04:50:31.9173330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9173481Z         out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:50:31.9173543Z         
2025-03-14T04:50:31.9173820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9174264Z         x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9174336Z         
2025-03-14T04:50:31.9174613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9176256Z         x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9176327Z         
2025-03-14T04:50:31.9176623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9176769Z         out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:50:31.9176832Z         
2025-03-14T04:50:31.9177098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9177544Z         x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9177615Z         
2025-03-14T04:50:31.9177891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9179547Z         x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9179619Z         
2025-03-14T04:50:31.9179919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9180101Z         out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:50:31.9180165Z         
2025-03-14T04:50:31.9180428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9180849Z         x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9180916Z         
2025-03-14T04:50:31.9181179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9182750Z         x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9182826Z         
2025-03-14T04:50:31.9183117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9183278Z         x_135 += out_79;  out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:50:31.9183343Z         
2025-03-14T04:50:31.9183642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9183783Z         out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:50:31.9183854Z         
2025-03-14T04:50:31.9184112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9184535Z         x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9184600Z         
2025-03-14T04:50:31.9184868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9186451Z         x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9186546Z         
2025-03-14T04:50:31.9186839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9186971Z         out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:50:31.9187043Z         
2025-03-14T04:50:31.9187289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9187719Z         x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9187781Z         
2025-03-14T04:50:31.9188049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9189581Z         x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9189651Z         
2025-03-14T04:50:31.9189939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9190072Z         out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:50:31.9190144Z         
2025-03-14T04:50:31.9190392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9190825Z         x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9190887Z         
2025-03-14T04:50:31.9191158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9192743Z         x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9192805Z         
2025-03-14T04:50:31.9193088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9193294Z         x_141 += out_83;  out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:50:31.9193363Z         
2025-03-14T04:50:31.9193641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9193783Z         out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:50:31.9193844Z         
2025-03-14T04:50:31.9194099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9194512Z         x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9194584Z         
2025-03-14T04:50:31.9194842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9196376Z         x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9196445Z         
2025-03-14T04:50:31.9196724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9196862Z         out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:50:31.9196923Z         
2025-03-14T04:50:31.9197174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9197598Z         x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9197671Z         
2025-03-14T04:50:31.9197938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9199629Z         x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9199729Z         
2025-03-14T04:50:31.9200023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9200160Z         out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:50:31.9200222Z         
2025-03-14T04:50:31.9200492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9200947Z         x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9201019Z         
2025-03-14T04:50:31.9201296Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9202946Z         x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9203022Z         
2025-03-14T04:50:31.9203315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9203476Z         x_147 += out_87;  out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:50:31.9203540Z         
2025-03-14T04:50:31.9203842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9203984Z         out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:50:31.9204053Z         
2025-03-14T04:50:31.9204318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9204769Z         x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9204841Z         
2025-03-14T04:50:31.9205122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9206810Z         x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9206917Z         
2025-03-14T04:50:31.9207222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9207360Z         out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:50:31.9207431Z         
2025-03-14T04:50:31.9207694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9208148Z         x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9208223Z         
2025-03-14T04:50:31.9208496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9210144Z         x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9210212Z         
2025-03-14T04:50:31.9210516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9210655Z         out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:50:31.9210723Z         
2025-03-14T04:50:31.9210983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9211525Z         x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9211618Z         
2025-03-14T04:50:31.9211922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9213774Z         x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9213871Z         
2025-03-14T04:50:31.9214173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9214333Z         x_153 += out_91;  out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:50:31.9214399Z         
2025-03-14T04:50:31.9214703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9214850Z         out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:50:31.9214923Z         
2025-03-14T04:50:31.9215182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9215638Z         x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9215702Z         
2025-03-14T04:50:31.9215986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9217634Z         x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9217709Z         
2025-03-14T04:50:31.9218012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9218149Z         out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:50:31.9218223Z         
2025-03-14T04:50:31.9218480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9218939Z         x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9219001Z         
2025-03-14T04:50:31.9219282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9220926Z         x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9221023Z         
2025-03-14T04:50:31.9221310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9221442Z         out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:50:31.9221512Z         
2025-03-14T04:50:31.9221762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9222195Z         x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9222262Z         
2025-03-14T04:50:31.9222532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9224114Z         x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9224191Z         
2025-03-14T04:50:31.9224477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9224625Z         x_159 += out_95;  out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:50:31.9224696Z         
2025-03-14T04:50:31.9224977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9225125Z         out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:50:31.9225190Z         
2025-03-14T04:50:31.9225449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9225866Z         x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9225936Z         
2025-03-14T04:50:31.9226199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9227772Z         x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9227869Z         
2025-03-14T04:50:31.9228153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9228298Z         out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:50:31.9228360Z         
2025-03-14T04:50:31.9228613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9229044Z         x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9229113Z         
2025-03-14T04:50:31.9229373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9230924Z         x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9230997Z         
2025-03-14T04:50:31.9231279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9231426Z         out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:50:31.9231488Z         
2025-03-14T04:50:31.9231742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9232177Z         x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9232246Z         
2025-03-14T04:50:31.9232503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9234081Z         x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9234176Z         
2025-03-14T04:50:31.9234459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9234621Z         x_165 += out_99;  out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:50:31.9234682Z         
2025-03-14T04:50:31.9234969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9235117Z         out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:50:31.9235184Z         
2025-03-14T04:50:31.9235440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9235866Z         x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9235927Z         
2025-03-14T04:50:31.9236197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9237772Z         x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9237835Z         
2025-03-14T04:50:31.9238123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9238256Z         out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:50:31.9238327Z         
2025-03-14T04:50:31.9238574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9239004Z         x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9239064Z         
2025-03-14T04:50:31.9239335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9240918Z         x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9241007Z         
2025-03-14T04:50:31.9241304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9241436Z         out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:50:31.9241509Z         
2025-03-14T04:50:31.9241761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9242190Z         x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9242252Z         
2025-03-14T04:50:31.9242529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9244070Z         x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9244135Z         
2025-03-14T04:50:31.9244426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9244594Z         x_171 += out_103;  out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:50:31.9244662Z         
2025-03-14T04:50:31.9244941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9245088Z         out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:50:31.9245146Z         
2025-03-14T04:50:31.9245401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9245819Z         x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9245908Z         
2025-03-14T04:50:31.9246179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9247764Z         x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9247834Z         
2025-03-14T04:50:31.9248118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9248264Z         out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:50:31.9248324Z         
2025-03-14T04:50:31.9248580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9249013Z         x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9249073Z         
2025-03-14T04:50:31.9249372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9251032Z         x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9251105Z         
2025-03-14T04:50:31.9251419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9251622Z         out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:50:31.9251695Z         
2025-03-14T04:50:31.9251967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9252433Z         x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9252494Z         
2025-03-14T04:50:31.9252802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9254361Z         x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9254458Z         
2025-03-14T04:50:31.9254745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9254900Z         x_177 += out_107;  out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:50:31.9254973Z         
2025-03-14T04:50:31.9255258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9255409Z         out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:50:31.9255481Z         
2025-03-14T04:50:31.9255728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9256139Z         x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9256205Z         
2025-03-14T04:50:31.9256463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9258041Z         x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9258112Z         
2025-03-14T04:50:31.9258400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9258542Z         out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:50:31.9258603Z         
2025-03-14T04:50:31.9258858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9259318Z         x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9259390Z         
2025-03-14T04:50:31.9259651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9261346Z         x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9261418Z         
2025-03-14T04:50:31.9261704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9261842Z         out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:50:31.9261903Z         
2025-03-14T04:50:31.9262156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9262581Z         x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9262651Z         
2025-03-14T04:50:31.9262911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9264472Z         x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9264542Z         
2025-03-14T04:50:31.9264816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9264979Z         x_183 += out_111;  out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:50:31.9265043Z         
2025-03-14T04:50:31.9265327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9265465Z         out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:50:31.9265532Z         
2025-03-14T04:50:31.9265777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9266252Z         x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9266353Z         
2025-03-14T04:50:31.9266622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9268172Z         x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9268234Z         
2025-03-14T04:50:31.9268532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9268664Z         out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:50:31.9268734Z         
2025-03-14T04:50:31.9268980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9269417Z         x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9269483Z         
2025-03-14T04:50:31.9269754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9271291Z         x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9271356Z         
2025-03-14T04:50:31.9271648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9271779Z         out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:50:31.9271850Z         
2025-03-14T04:50:31.9272101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9272567Z         x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9272632Z         
2025-03-14T04:50:31.9272940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9274460Z         x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9274524Z         
2025-03-14T04:50:31.9274802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9274950Z         x_189 += out_115;  out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:50:31.9275017Z         
2025-03-14T04:50:31.9275292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9275440Z         out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:50:31.9275501Z         
2025-03-14T04:50:31.9275949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:50:31.9276102Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:50:31.9276173Z         
2025-03-14T04:50:31.9276467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:31.9276612Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:50:31.9276673Z         
2025-03-14T04:50:31.9277120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:50:31.9277278Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:50:31.9277339Z         
2025-03-14T04:50:31.9277636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:31.9277774Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:50:31.9277840Z         
2025-03-14T04:50:31.9278210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:50:31.9278398Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:50:31.9278493Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:50:31.9278617Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:50:31.9278677Z         
2025-03-14T04:50:31.9279043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:50:31.9279206Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:50:31.9279273Z         
2025-03-14T04:50:31.9279596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:50:31.9279718Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:50:31.9279780Z         
2025-03-14T04:50:31.9280162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:50:31.9280374Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:50:31.9280444Z         
2025-03-14T04:50:31.9280854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:50:31.9280987Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:50:31.9281415Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:50:31.9281541Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:50:31.9281649Z         x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:50:31.9281718Z         
2025-03-14T04:50:31.9282011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:50:31.9282138Z         tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32);  x_190 = None
2025-03-14T04:50:31.9282200Z         
2025-03-14T04:50:31.9282450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9283207Z         x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:50:31.9283278Z         
2025-03-14T04:50:31.9283547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:50:31.9283727Z         x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False);  x_191 = None
2025-03-14T04:50:31.9283798Z         
2025-03-14T04:50:31.9284167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:50:31.9285029Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:50:31.9285092Z         
2025-03-14T04:50:31.9285453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:50:31.9286286Z         x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:50:31.9286353Z         
2025-03-14T04:50:31.9286693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:50:31.9286838Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:50:31.9286978Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:50:31.9287036Z         
2025-03-14T04:50:31.9287451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:50:31.9287601Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75);  x_193 = None
2025-03-14T04:50:31.9287773Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:50:31.9287945Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:50:31.9288011Z         
2025-03-14T04:50:31.9288409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:50:31.9288619Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:50:31.9288680Z         
2025-03-14T04:50:31.9289116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:50:31.9289260Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:50:31.9289411Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:50:31.9289549Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:50:31.9289618Z         
2025-03-14T04:50:31.9289988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:50:31.9290165Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:50:31.9290226Z         
2025-03-14T04:50:31.9290547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:50:31.9290693Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:50:31.9290763Z         
2025-03-14T04:50:31.9291147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:50:31.9291291Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:50:31.9291420Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:50:31.9291670Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:50:31.9291738Z         
2025-03-14T04:50:31.9292079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:50:31.9292214Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:50:31.9292345Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:50:31.9292496Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:50:31.9292557Z         
2025-03-14T04:50:31.9292874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:50:31.9292997Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:50:31.9293098Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:50:31.9293225Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:50:31.9293296Z         
2025-03-14T04:50:31.9293618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:50:31.9293779Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:50:31.9293866Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:50:31.9293995Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:50:31.9294059Z         
2025-03-14T04:50:31.9294421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:50:31.9294571Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:50:31.9294694Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:50:31.9294755Z         
2025-03-14T04:50:31.9295061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:50:31.9295210Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:50:31.9295323Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:50:31.9295382Z         
2025-03-14T04:50:31.9295683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:50:31.9295833Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:50:31.9295950Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:50:31.9296013Z         
2025-03-14T04:50:31.9296321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:50:31.9296503Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:50:31.9296619Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:50:31.9296681Z         
2025-03-14T04:50:31.9297020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:50:31.9297183Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:50:31.9297252Z         
2025-03-14T04:50:31.9297583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:50:31.9297751Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:50:31.9297811Z         
2025-03-14T04:50:31.9298163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:50:31.9298299Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:50:31.9298428Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:50:31.9298587Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:50:31.9298724Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:50:31.9298794Z         
2025-03-14T04:50:31.9299138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:50:31.9299282Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:50:31.9299399Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:50:31.9299551Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:50:31.9299683Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:50:31.9299753Z         
2025-03-14T04:50:31.9300081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:50:31.9300201Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:50:31.9300359Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:50:31.9300492Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:50:31.9300552Z         
2025-03-14T04:50:31.9300888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:50:31.9301000Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:50:31.9301167Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:50:31.9301297Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:50:31.9301364Z         
2025-03-14T04:50:31.9301668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:50:31.9301769Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:50:31.9301881Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:50:31.9301947Z         
2025-03-14T04:50:31.9302251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:50:31.9302345Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:50:31.9302452Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:50:31.9302518Z         
2025-03-14T04:50:31.9302841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:50:31.9302960Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:50:31.9303119Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:50:31.9303189Z         
2025-03-14T04:50:31.9303485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:50:31.9303599Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:50:31.9303721Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:50:31.9303789Z         
2025-03-14T04:50:31.9304138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:50:31.9304323Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:50:31.9304383Z         
2025-03-14T04:50:31.9304723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:50:31.9304880Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:50:31.9304948Z         
2025-03-14T04:50:31.9305325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:50:31.9305502Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:50:31.9305564Z         
2025-03-14T04:50:31.9306051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:50:31.9306185Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:50:31.9306254Z         
2025-03-14T04:50:31.9306544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:31.9306684Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:50:31.9306744Z         
2025-03-14T04:50:31.9307182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:50:31.9307302Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:50:31.9307404Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:50:31.9307528Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:50:31.9307596Z         
2025-03-14T04:50:31.9308106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:50:31.9308265Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:50:31.9308503Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:50:31.9308565Z         
2025-03-14T04:50:31.9309058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:50:31.9309223Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:50:31.9309325Z         
2025-03-14T04:50:31.9309616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:31.9309766Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:50:31.9309827Z         
2025-03-14T04:50:31.9310213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:50:31.9310354Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:50:31.9310420Z         
2025-03-14T04:50:31.9310714Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:50:31.9310866Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:50:31.9310931Z         
2025-03-14T04:50:31.9311308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:50:31.9311443Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:50:31.9311511Z         
2025-03-14T04:50:31.9311988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:50:31.9312129Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:50:31.9312245Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:50:31.9312403Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:50:31.9312536Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:50:31.9312603Z         
2025-03-14T04:50:31.9312966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:50:31.9313086Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:50:31.9313147Z         
2025-03-14T04:50:31.9313162Z 
2025-03-14T04:50:31.9313250Z class GraphModule(torch.nn.Module):
2025-03-14T04:50:31.9405205Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:50:31.9406421Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:50:31.9406777Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9407193Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9407597Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9407941Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9408296Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9408608Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9409018Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9409420Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9409843Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9410222Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9410578Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9410996Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9411398Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9411805Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9412201Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9412554Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9412946Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9413350Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9413721Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9414066Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9414436Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:50:31.9414847Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9415254Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9415656Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:50:31.9416046Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:50:31.9416361Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9416775Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9417201Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9417605Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9418002Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9418316Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9418707Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9419081Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9419436Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9419788Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9420112Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9420481Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9420912Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9421267Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9421588Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9421871Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9422204Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9422539Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9422852Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9423163Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9423440Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9423782Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9424142Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9424484Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9424796Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9425066Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9425404Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9425738Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9426066Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9426376Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9426666Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9427013Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9427347Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9427682Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9427992Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9428279Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9428617Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9428957Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9429273Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9429588Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9429874Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9430243Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9430589Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9430941Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9431257Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9431558Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:50:31.9431920Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9432273Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9432605Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:50:31.9432927Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:50:31.9433203Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9433543Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9433869Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9434187Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9434487Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9434771Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9435106Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9435450Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9435773Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9436081Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9436403Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9436744Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9437115Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9437433Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9437747Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9438030Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9438375Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9438717Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9439034Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9439351Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9439629Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9439972Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9440306Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9440631Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9440939Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9441227Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9441572Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9441910Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9442234Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9442542Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9442856Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9443194Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9443570Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9443889Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9444209Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9444497Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9444833Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9445177Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9445493Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9445807Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9446091Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9446439Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9446793Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9447135Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9447466Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9447767Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9448133Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9448485Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9448828Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9449183Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9449493Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9449901Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9450288Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9450653Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9451010Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9451332Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9451789Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9452199Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9452571Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9452922Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9453220Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:50:31.9453579Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9453932Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9454268Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:50:31.9454602Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:50:31.9454889Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9455238Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9455576Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9455901Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9456241Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9456559Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9456905Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9457238Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9457568Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9457875Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9458165Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9458505Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9458834Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9459144Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9459451Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9459734Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9460063Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9460396Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9460828Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9461156Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9461447Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9461808Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9462138Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9462523Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9462836Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9463151Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9463493Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9463820Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9464145Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9464450Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9464736Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9465072Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9465408Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9465735Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9466040Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9466331Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9466663Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9467003Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9467324Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9467646Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9467932Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9468280Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9468656Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9468976Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9469334Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9469610Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9469947Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9470274Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9470591Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9470897Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9471180Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9471517Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9471848Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9472166Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9472468Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9472749Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9473078Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9473412Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9473730Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9474056Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9474338Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9474673Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9475059Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9475376Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9475721Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9476008Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9476357Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9476698Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9477026Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9477347Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9477636Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9477985Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9478324Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9478656Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9478965Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9479256Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9479596Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9479946Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9480279Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9480592Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9480882Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9481252Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9481598Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9481955Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9482278Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9482557Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9482907Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9483255Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9483578Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9483901Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9484186Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9484532Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9484870Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9485202Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9485514Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9485814Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9486204Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9486571Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9486917Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9487257Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9487578Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9488007Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9488407Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9488794Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9489156Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9489484Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9489890Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9490278Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9490615Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9490948Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9491249Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9491699Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9492083Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9492447Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9492794Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9493094Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9493458Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9493814Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9494158Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9494499Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9494840Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9495206Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9495602Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9495946Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9496271Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9496579Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9496937Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9497301Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9497633Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9497965Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9498267Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9498631Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9498993Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9499328Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9499664Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9499971Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9500344Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9500704Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9501052Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9501381Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9501726Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9502103Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9502505Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9502850Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9503181Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9503494Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9503858Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9504217Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9504559Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9504905Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9505220Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9505588Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9505951Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9506294Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9506636Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9506941Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9507311Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9507675Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9508019Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9508385Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9508690Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9509096Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9509447Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9509793Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9510133Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9510459Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9510851Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9511206Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9511546Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9511875Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9512187Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9512552Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9512896Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9513219Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9513540Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9513835Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9514184Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9514531Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9514880Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9515195Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9515513Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9515881Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9516235Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9516567Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9516886Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9517174Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9517539Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9517902Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9518229Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9518552Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9518880Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9519255Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9519599Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9519939Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9520277Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9520584Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9520943Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9521338Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9521674Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9522035Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9522322Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9522669Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9523036Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9523388Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9523732Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9524042Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9524418Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9524788Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9525147Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9525478Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9525799Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9526174Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9526557Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9526916Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9527253Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9527571Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9527938Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9528356Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9528705Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9529068Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9529387Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9529753Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9530140Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9530493Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9530827Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9531138Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9531593Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9531969Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9532334Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9532669Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9532967Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9533334Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9533688Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9534036Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9534364Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9534672Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9535077Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9535444Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9535819Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9536129Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9536419Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9536759Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9537100Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9537421Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9537738Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9538021Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9538384Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9538729Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9539049Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9539363Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9539646Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9539993Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9540334Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9540663Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9540970Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9541286Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9541637Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9542003Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9542332Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9542642Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9542934Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9543273Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9543617Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9543933Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9544248Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9544541Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9544881Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9545225Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9545542Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9545854Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9546139Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9546485Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9546824Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9547151Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9547468Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9547781Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9548160Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9548498Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9548823Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9549136Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9549430Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9549775Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9550127Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9550444Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9550748Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9551032Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9551366Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9551700Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9552064Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9552435Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9552735Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9553118Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9553469Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9554100Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9554466Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9554790Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9555197Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9555557Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9555970Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9556304Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9556641Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9557003Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9557380Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9557779Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9558107Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9558451Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9558802Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9559178Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9559537Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9559906Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9560214Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9560700Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9561097Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9561610Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9562000Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9562369Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:50:31.9562776Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9563135Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9563528Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:50:31.9563893Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:50:31.9564628Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:50:31.9565880Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9566341Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9566795Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:50:31.9567151Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:50:31.9567526Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:50:31.9567898Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:50:31.9568312Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:50:31.9568706Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:50:31.9569067Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:50:31.9569491Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:50:31.9569845Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:50:31.9570283Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:50:31.9570675Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:50:31.9571171Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:50:31.9571698Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:50:31.9572096Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:50:31.9572213Z         
2025-03-14T04:50:31.9572538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9573114Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9573201Z         
2025-03-14T04:50:31.9573526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9575034Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9575141Z         
2025-03-14T04:50:31.9575514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:50:31.9575677Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:50:31.9575787Z         
2025-03-14T04:50:31.9576178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:50:31.9576460Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:50:31.9576569Z         
2025-03-14T04:50:31.9576887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9577346Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9577462Z         
2025-03-14T04:50:31.9577761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9579487Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9579657Z         
2025-03-14T04:50:31.9579986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9580179Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:50:31.9580262Z         
2025-03-14T04:50:31.9580588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9581068Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9581182Z         
2025-03-14T04:50:31.9600545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9602238Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9602325Z         
2025-03-14T04:50:31.9602663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9602821Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:50:31.9602885Z         
2025-03-14T04:50:31.9603171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9603630Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9603704Z         
2025-03-14T04:50:31.9603991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9605665Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9605783Z         
2025-03-14T04:50:31.9606045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9606507Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:50:31.9606575Z         
2025-03-14T04:50:31.9606871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9608600Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9608671Z         
2025-03-14T04:50:31.9608978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9609135Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:50:31.9609206Z         
2025-03-14T04:50:31.9609511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9609678Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:50:31.9609741Z         
2025-03-14T04:50:31.9610016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9610474Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9610548Z         
2025-03-14T04:50:31.9610827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9612683Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9612806Z         
2025-03-14T04:50:31.9613109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9613271Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:50:31.9613335Z         
2025-03-14T04:50:31.9613608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9614062Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9614138Z         
2025-03-14T04:50:31.9614433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9616130Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9616209Z         
2025-03-14T04:50:31.9616515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9616677Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:50:31.9616745Z         
2025-03-14T04:50:31.9617022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9617497Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9617574Z         
2025-03-14T04:50:31.9617857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9619592Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9619698Z         
2025-03-14T04:50:31.9620011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9620177Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:50:31.9620240Z         
2025-03-14T04:50:31.9620534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9620683Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:50:31.9620759Z         
2025-03-14T04:50:31.9621010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9621450Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9621519Z         
2025-03-14T04:50:31.9621782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9623354Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9623418Z         
2025-03-14T04:50:31.9623708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9623848Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:50:31.9623919Z         
2025-03-14T04:50:31.9624168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9624615Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9624686Z         
2025-03-14T04:50:31.9624950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9626537Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9626627Z         
2025-03-14T04:50:31.9626919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9627055Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:50:31.9627123Z         
2025-03-14T04:50:31.9627373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9627816Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9627889Z         
2025-03-14T04:50:31.9628156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9629727Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9629794Z         
2025-03-14T04:50:31.9630085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9630250Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:50:31.9630315Z         
2025-03-14T04:50:31.9630608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9630767Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:50:31.9630839Z         
2025-03-14T04:50:31.9631094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9631536Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9631601Z         
2025-03-14T04:50:31.9631874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9633457Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9633550Z         
2025-03-14T04:50:31.9633834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9633973Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:50:31.9634041Z         
2025-03-14T04:50:31.9634285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9634714Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9634776Z         
2025-03-14T04:50:31.9635043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9636550Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9636619Z         
2025-03-14T04:50:31.9636906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9637044Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:50:31.9637111Z         
2025-03-14T04:50:31.9637357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9637785Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9637847Z         
2025-03-14T04:50:31.9638112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9639677Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9639774Z         
2025-03-14T04:50:31.9640029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9640475Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:50:31.9640545Z         
2025-03-14T04:50:31.9640805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9642407Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9642474Z         
2025-03-14T04:50:31.9642744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9642897Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:50:31.9642957Z         
2025-03-14T04:50:31.9643246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9643394Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:50:31.9643461Z         
2025-03-14T04:50:31.9643708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9644149Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9644212Z         
2025-03-14T04:50:31.9644481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9646064Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9646190Z         
2025-03-14T04:50:31.9646479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9646619Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:50:31.9646689Z         
2025-03-14T04:50:31.9646935Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9647378Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9647445Z         
2025-03-14T04:50:31.9647722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9649347Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9649419Z         
2025-03-14T04:50:31.9649728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9649878Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:50:31.9649954Z         
2025-03-14T04:50:31.9650215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9650680Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9650750Z         
2025-03-14T04:50:31.9651034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9652896Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9653018Z         
2025-03-14T04:50:31.9653344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9653519Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:50:31.9653593Z         
2025-03-14T04:50:31.9653888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9654052Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:50:31.9654123Z         
2025-03-14T04:50:31.9654395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9654849Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9654928Z         
2025-03-14T04:50:31.9655206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9656837Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9656913Z         
2025-03-14T04:50:31.9657208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9657362Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:50:31.9657426Z         
2025-03-14T04:50:31.9657696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9658154Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9658229Z         
2025-03-14T04:50:31.9658502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9660166Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9660268Z         
2025-03-14T04:50:31.9660710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9660876Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:50:31.9660952Z         
2025-03-14T04:50:31.9661226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9661667Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9661738Z         
2025-03-14T04:50:31.9662002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9663556Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9663628Z         
2025-03-14T04:50:31.9663898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9664061Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:50:31.9664138Z         
2025-03-14T04:50:31.9664418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9664577Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:50:31.9664638Z         
2025-03-14T04:50:31.9664892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9665322Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9665392Z         
2025-03-14T04:50:31.9665652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9667331Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9667475Z         
2025-03-14T04:50:31.9667759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9667911Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:50:31.9667972Z         
2025-03-14T04:50:31.9668225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9668658Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9668728Z         
2025-03-14T04:50:31.9668987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9670535Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9670607Z         
2025-03-14T04:50:31.9670887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9671032Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:50:31.9671094Z         
2025-03-14T04:50:31.9671346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9671776Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9671847Z         
2025-03-14T04:50:31.9672107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9673675Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9673780Z         
2025-03-14T04:50:31.9674058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9674218Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:50:31.9674280Z         
2025-03-14T04:50:31.9674569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9674718Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:50:31.9674788Z         
2025-03-14T04:50:31.9675038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9675472Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9675534Z         
2025-03-14T04:50:31.9675803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9677361Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9677426Z         
2025-03-14T04:50:31.9677714Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9677853Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:50:31.9677920Z         
2025-03-14T04:50:31.9678166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9678603Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9678664Z         
2025-03-14T04:50:31.9678932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9680517Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9680605Z         
2025-03-14T04:50:31.9680894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9681028Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:50:31.9681096Z         
2025-03-14T04:50:31.9681343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9681780Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9681841Z         
2025-03-14T04:50:31.9682110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9683625Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9683689Z         
2025-03-14T04:50:31.9683937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9684373Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:50:31.9684442Z         
2025-03-14T04:50:31.9684702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9686327Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9686423Z         
2025-03-14T04:50:31.9686703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9686851Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:50:31.9686913Z         
2025-03-14T04:50:31.9687203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9687344Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:50:31.9687412Z         
2025-03-14T04:50:31.9687665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9688092Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9688157Z         
2025-03-14T04:50:31.9688428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9689958Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9690022Z         
2025-03-14T04:50:31.9690310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9690441Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:50:31.9690511Z         
2025-03-14T04:50:31.9690760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9691187Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9691260Z         
2025-03-14T04:50:31.9691574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9693181Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9693268Z         
2025-03-14T04:50:31.9693557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9693687Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:50:31.9693755Z         
2025-03-14T04:50:31.9694001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9694435Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9694508Z         
2025-03-14T04:50:31.9694775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9696317Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9696381Z         
2025-03-14T04:50:31.9696664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9696810Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:50:31.9696882Z         
2025-03-14T04:50:31.9697161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9697307Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:50:31.9697371Z         
2025-03-14T04:50:31.9697621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9698039Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9698101Z         
2025-03-14T04:50:31.9698365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9699926Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9700025Z         
2025-03-14T04:50:31.9700319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9700449Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:50:31.9700517Z         
2025-03-14T04:50:31.9700771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9701194Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9701259Z         
2025-03-14T04:50:31.9701530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9703066Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9703136Z         
2025-03-14T04:50:31.9703427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9703554Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:50:31.9703620Z         
2025-03-14T04:50:31.9703871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9704300Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9704364Z         
2025-03-14T04:50:31.9704635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9706189Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9706290Z         
2025-03-14T04:50:31.9706574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9706721Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:50:31.9706788Z         
2025-03-14T04:50:31.9707068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9707214Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:50:31.9707275Z         
2025-03-14T04:50:31.9707530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9707947Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9708015Z         
2025-03-14T04:50:31.9708277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9709806Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9709876Z         
2025-03-14T04:50:31.9710155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9710292Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:50:31.9710354Z         
2025-03-14T04:50:31.9710608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9711034Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9711103Z         
2025-03-14T04:50:31.9711366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9712920Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9713016Z         
2025-03-14T04:50:31.9713298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9713435Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:50:31.9713494Z         
2025-03-14T04:50:31.9713752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9714173Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9714244Z         
2025-03-14T04:50:31.9714505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9716040Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9716111Z         
2025-03-14T04:50:31.9716388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9716537Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:50:31.9716602Z         
2025-03-14T04:50:31.9716909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9717052Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:50:31.9717122Z         
2025-03-14T04:50:31.9717387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9717838Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9717900Z         
2025-03-14T04:50:31.9718172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9719746Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9719838Z         
2025-03-14T04:50:31.9720134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9720267Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:50:31.9720335Z         
2025-03-14T04:50:31.9720578Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9721006Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9721066Z         
2025-03-14T04:50:31.9721332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9722858Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9722922Z         
2025-03-14T04:50:31.9723209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9723339Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:50:31.9723409Z         
2025-03-14T04:50:31.9723658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9724087Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9724150Z         
2025-03-14T04:50:31.9724417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9725987Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9726077Z         
2025-03-14T04:50:31.9726363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9726503Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:50:31.9726572Z         
2025-03-14T04:50:31.9726853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9726997Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:50:31.9727059Z         
2025-03-14T04:50:31.9727311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9727726Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9727795Z         
2025-03-14T04:50:31.9728054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9729591Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9729662Z         
2025-03-14T04:50:31.9729942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9730077Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:50:31.9730136Z         
2025-03-14T04:50:31.9730390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9730811Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9730877Z         
2025-03-14T04:50:31.9731135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9732824Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9732925Z         
2025-03-14T04:50:31.9733207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9733349Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:50:31.9733411Z         
2025-03-14T04:50:31.9733664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9734087Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9734157Z         
2025-03-14T04:50:31.9734417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9735961Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9736034Z         
2025-03-14T04:50:31.9736310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9736461Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:50:31.9736525Z         
2025-03-14T04:50:31.9736813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9736954Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:50:31.9737022Z         
2025-03-14T04:50:31.9737266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9737684Z         x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9737754Z         
2025-03-14T04:50:31.9738052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9739545Z         x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9739635Z         
2025-03-14T04:50:31.9739924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9740055Z         out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:50:31.9740130Z         
2025-03-14T04:50:31.9740375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9740789Z         x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9740861Z         
2025-03-14T04:50:31.9741118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9742602Z         x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9742668Z         
2025-03-14T04:50:31.9742955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9743083Z         out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:50:31.9743157Z         
2025-03-14T04:50:31.9743404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9743824Z         x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9743893Z         
2025-03-14T04:50:31.9744150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9745673Z         x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9745769Z         
2025-03-14T04:50:31.9746044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9746192Z         x_93 += out_51;  out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:50:31.9746253Z         
2025-03-14T04:50:31.9746533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9746669Z         out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:50:31.9746735Z         
2025-03-14T04:50:31.9746978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9747386Z         x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9747445Z         
2025-03-14T04:50:31.9747707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9749198Z         x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9749271Z         
2025-03-14T04:50:31.9749553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9749682Z         out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:50:31.9749747Z         
2025-03-14T04:50:31.9749984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9750401Z         x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9750461Z         
2025-03-14T04:50:31.9750749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9752223Z         x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9752317Z         
2025-03-14T04:50:31.9752599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9752724Z         out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:50:31.9752793Z         
2025-03-14T04:50:31.9753030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9753448Z         x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9753507Z         
2025-03-14T04:50:31.9753769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9755252Z         x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9755321Z         
2025-03-14T04:50:31.9755594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9755732Z         x_99 += out_55;  out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:50:31.9755800Z         
2025-03-14T04:50:31.9756071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9756210Z         out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:50:31.9756269Z         
2025-03-14T04:50:31.9756515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9756921Z         x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9757014Z         
2025-03-14T04:50:31.9757269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9758823Z         x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9758892Z         
2025-03-14T04:50:31.9759180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9759321Z         out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:50:31.9759379Z         
2025-03-14T04:50:31.9759625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9760033Z         x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9760096Z         
2025-03-14T04:50:31.9760353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9761987Z         x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9762065Z         
2025-03-14T04:50:31.9762347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9762493Z         out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:50:31.9762553Z         
2025-03-14T04:50:31.9762804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9763229Z         x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9763297Z         
2025-03-14T04:50:31.9763638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9765172Z         x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9765277Z         
2025-03-14T04:50:31.9765561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9765721Z         x_105 += out_59;  out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:50:31.9765785Z         
2025-03-14T04:50:31.9766073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9766210Z         out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:50:31.9766278Z         
2025-03-14T04:50:31.9766523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9766949Z         x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9767012Z         
2025-03-14T04:50:31.9767282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9768835Z         x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9768896Z         
2025-03-14T04:50:31.9769187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9769319Z         out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:50:31.9769385Z         
2025-03-14T04:50:31.9769632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9770122Z         x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9770183Z         
2025-03-14T04:50:31.9770454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9772084Z         x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9772155Z         
2025-03-14T04:50:31.9772467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9772617Z         out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:50:31.9772685Z         
2025-03-14T04:50:31.9772937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9773370Z         x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9773431Z         
2025-03-14T04:50:31.9773705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9775272Z         x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9775339Z         
2025-03-14T04:50:31.9775629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9775776Z         x_111 += out_63;  out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:50:31.9775845Z         
2025-03-14T04:50:31.9776117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9776258Z         out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:50:31.9776317Z         
2025-03-14T04:50:31.9776564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9777011Z         x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9777104Z         
2025-03-14T04:50:31.9777361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9778914Z         x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9778986Z         
2025-03-14T04:50:31.9779263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9779398Z         out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:50:31.9779457Z         
2025-03-14T04:50:31.9779708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9780123Z         x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9780190Z         
2025-03-14T04:50:31.9780456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9781968Z         x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9782037Z         
2025-03-14T04:50:31.9782314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9782451Z         out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:50:31.9782511Z         
2025-03-14T04:50:31.9782759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9783209Z         x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9783270Z         
2025-03-14T04:50:31.9783528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9785060Z         x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9785130Z         
2025-03-14T04:50:31.9785400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9785551Z         x_117 += out_67;  out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:50:31.9785611Z         
2025-03-14T04:50:31.9785892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9786033Z         out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:50:31.9786092Z         
2025-03-14T04:50:31.9786339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9786744Z         x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9786812Z         
2025-03-14T04:50:31.9787066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9788593Z         x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9788661Z         
2025-03-14T04:50:31.9788933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9789068Z         out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:50:31.9789126Z         
2025-03-14T04:50:31.9789374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9789816Z         x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9789911Z         
2025-03-14T04:50:31.9790165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9791656Z         x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9791728Z         
2025-03-14T04:50:31.9792002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9792139Z         out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:50:31.9792198Z         
2025-03-14T04:50:31.9792451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9792876Z         x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9792948Z         
2025-03-14T04:50:31.9793208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9794748Z         x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9794822Z         
2025-03-14T04:50:31.9795095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9795246Z         x_123 += out_71;  out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:50:31.9795307Z         
2025-03-14T04:50:31.9795594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9795729Z         out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:50:31.9795840Z         
2025-03-14T04:50:31.9796097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9796528Z         x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9796623Z         
2025-03-14T04:50:31.9796895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9798437Z         x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9798514Z         
2025-03-14T04:50:31.9798809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9798949Z         out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:50:31.9799022Z         
2025-03-14T04:50:31.9799276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9799716Z         x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9799783Z         
2025-03-14T04:50:31.9800056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9801617Z         x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9801684Z         
2025-03-14T04:50:31.9801977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9802112Z         out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:50:31.9802182Z         
2025-03-14T04:50:31.9802461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9802890Z         x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9802984Z         
2025-03-14T04:50:31.9803252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9804784Z         x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9804851Z         
2025-03-14T04:50:31.9805133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9805279Z         x_129 += out_75;  out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:50:31.9805346Z         
2025-03-14T04:50:31.9805623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9805770Z         out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:50:31.9805831Z         
2025-03-14T04:50:31.9806085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9806500Z         x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9806571Z         
2025-03-14T04:50:31.9806830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9808457Z         x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9808532Z         
2025-03-14T04:50:31.9808829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9809006Z         out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:50:31.9809072Z         
2025-03-14T04:50:31.9809343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9809817Z         x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9809889Z         
2025-03-14T04:50:31.9810162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9811871Z         x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9811958Z         
2025-03-14T04:50:31.9812276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9812426Z         out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:50:31.9812494Z         
2025-03-14T04:50:31.9812793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9813242Z         x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9813318Z         
2025-03-14T04:50:31.9813599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9815310Z         x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9815386Z         
2025-03-14T04:50:31.9815678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9815841Z         x_135 += out_79;  out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:50:31.9815907Z         
2025-03-14T04:50:31.9816249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9816396Z         out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:50:31.9816499Z         
2025-03-14T04:50:31.9816759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9817207Z         x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9817282Z         
2025-03-14T04:50:31.9817554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9819189Z         x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9819256Z         
2025-03-14T04:50:31.9819562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9819702Z         out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:50:31.9819776Z         
2025-03-14T04:50:31.9820036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9820487Z         x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9820557Z         
2025-03-14T04:50:31.9820833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9822408Z         x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9822473Z         
2025-03-14T04:50:31.9822762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9822939Z         out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:50:31.9823010Z         
2025-03-14T04:50:31.9823261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9823722Z         x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9823793Z         
2025-03-14T04:50:31.9824057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9825643Z         x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9825715Z         
2025-03-14T04:50:31.9825988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9826145Z         x_141 += out_83;  out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:50:31.9826206Z         
2025-03-14T04:50:31.9826492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9826633Z         out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:50:31.9826701Z         
2025-03-14T04:50:31.9826946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9827368Z         x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9827430Z         
2025-03-14T04:50:31.9827703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9829268Z         x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9829341Z         
2025-03-14T04:50:31.9829660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9829794Z         out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:50:31.9829890Z         
2025-03-14T04:50:31.9830137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9830566Z         x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9830626Z         
2025-03-14T04:50:31.9830893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9832430Z         x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9832503Z         
2025-03-14T04:50:31.9832794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9832923Z         out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:50:31.9832994Z         
2025-03-14T04:50:31.9833241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9833671Z         x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9833744Z         
2025-03-14T04:50:31.9834007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9835518Z         x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9835589Z         
2025-03-14T04:50:31.9835893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9836036Z         x_147 += out_87;  out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:50:31.9836102Z         
2025-03-14T04:50:31.9836400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9836541Z         out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:50:31.9836600Z         
2025-03-14T04:50:31.9836847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9837259Z         x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9837330Z         
2025-03-14T04:50:31.9837585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9839123Z         x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9839192Z         
2025-03-14T04:50:31.9839471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9839612Z         out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:50:31.9839673Z         
2025-03-14T04:50:31.9839926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9840349Z         x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9840417Z         
2025-03-14T04:50:31.9840687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9842228Z         x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9842301Z         
2025-03-14T04:50:31.9842580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9842746Z         out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:50:31.9842806Z         
2025-03-14T04:50:31.9843054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9843464Z         x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9843531Z         
2025-03-14T04:50:31.9843786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9845299Z         x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9845369Z         
2025-03-14T04:50:31.9845636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9845792Z         x_153 += out_91;  out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:50:31.9845856Z         
2025-03-14T04:50:31.9846142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9846280Z         out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:50:31.9846348Z         
2025-03-14T04:50:31.9846590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9847012Z         x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9847084Z         
2025-03-14T04:50:31.9847343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9848932Z         x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9849023Z         
2025-03-14T04:50:31.9849315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9849448Z         out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:50:31.9849515Z         
2025-03-14T04:50:31.9849760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9850201Z         x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9850262Z         
2025-03-14T04:50:31.9850528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9852165Z         x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9852235Z         
2025-03-14T04:50:31.9852544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9852687Z         out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:50:31.9852757Z         
2025-03-14T04:50:31.9853003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9853433Z         x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9853506Z         
2025-03-14T04:50:31.9853764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9855354Z         x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9855419Z         
2025-03-14T04:50:31.9855706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9855887Z         x_159 += out_95;  out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:50:31.9855959Z         
2025-03-14T04:50:31.9856241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9856388Z         out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:50:31.9856448Z         
2025-03-14T04:50:31.9856699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9857127Z         x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9857189Z         
2025-03-14T04:50:31.9857458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9858979Z         x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9859052Z         
2025-03-14T04:50:31.9859331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9859475Z         out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:50:31.9859544Z         
2025-03-14T04:50:31.9859789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9860222Z         x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9860285Z         
2025-03-14T04:50:31.9860649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9862262Z         x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9862384Z         
2025-03-14T04:50:31.9862685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9862824Z         out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:50:31.9862891Z         
2025-03-14T04:50:31.9863139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9863581Z         x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9863644Z         
2025-03-14T04:50:31.9863915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9865464Z         x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9865536Z         
2025-03-14T04:50:31.9865817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9865969Z         x_165 += out_99;  out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:50:31.9866038Z         
2025-03-14T04:50:31.9866311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9866460Z         out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:50:31.9866518Z         
2025-03-14T04:50:31.9866767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9867177Z         x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9867246Z         
2025-03-14T04:50:31.9867504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9869109Z         x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9869212Z         
2025-03-14T04:50:31.9869492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9869631Z         out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:50:31.9869690Z         
2025-03-14T04:50:31.9869941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9870355Z         x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9870422Z         
2025-03-14T04:50:31.9870680Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9872184Z         x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9872252Z         
2025-03-14T04:50:31.9872530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9872669Z         out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:50:31.9872730Z         
2025-03-14T04:50:31.9872986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9873415Z         x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9873486Z         
2025-03-14T04:50:31.9873741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9875290Z         x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9875383Z         
2025-03-14T04:50:31.9875655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9875819Z         x_171 += out_103;  out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:50:31.9875880Z         
2025-03-14T04:50:31.9876161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9876302Z         out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:50:31.9876370Z         
2025-03-14T04:50:31.9876613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9877033Z         x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9877093Z         
2025-03-14T04:50:31.9877356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9878896Z         x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9878961Z         
2025-03-14T04:50:31.9879250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9879384Z         out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:50:31.9879456Z         
2025-03-14T04:50:31.9879709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9880137Z         x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9880200Z         
2025-03-14T04:50:31.9880461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9882005Z         x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9882096Z         
2025-03-14T04:50:31.9882387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9882529Z         out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:50:31.9882600Z         
2025-03-14T04:50:31.9882849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9883292Z         x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9883355Z         
2025-03-14T04:50:31.9883616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9885151Z         x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9885216Z         
2025-03-14T04:50:31.9885496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9885653Z         x_177 += out_107;  out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:50:31.9885723Z         
2025-03-14T04:50:31.9886005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9886154Z         out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:50:31.9886219Z         
2025-03-14T04:50:31.9886470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9886887Z         x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9886956Z         
2025-03-14T04:50:31.9887216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9888845Z         x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9888947Z         
2025-03-14T04:50:31.9889245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9889395Z         out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:50:31.9889460Z         
2025-03-14T04:50:31.9889727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9890177Z         x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9890249Z         
2025-03-14T04:50:31.9890534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9892212Z         x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9892292Z         
2025-03-14T04:50:31.9892590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9892739Z         out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:50:31.9892812Z         
2025-03-14T04:50:31.9893063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9893497Z         x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9893558Z         
2025-03-14T04:50:31.9893829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9895399Z         x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9895497Z         
2025-03-14T04:50:31.9895774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9895937Z         x_183 += out_111;  out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:50:31.9895999Z         
2025-03-14T04:50:31.9896285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9896435Z         out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:50:31.9896496Z         
2025-03-14T04:50:31.9896748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9897168Z         x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:50:31.9897236Z         
2025-03-14T04:50:31.9897503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9899036Z         x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9899108Z         
2025-03-14T04:50:31.9899394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9899536Z         out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:50:31.9899600Z         
2025-03-14T04:50:31.9899852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9900273Z         x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:50:31.9900341Z         
2025-03-14T04:50:31.9900601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9902166Z         x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9902264Z         
2025-03-14T04:50:31.9902553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9902698Z         out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:50:31.9902767Z         
2025-03-14T04:50:31.9903021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9903456Z         x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:50:31.9903527Z         
2025-03-14T04:50:31.9903792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:50:31.9905341Z         x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:50:31.9905415Z         
2025-03-14T04:50:31.9905694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:50:31.9905859Z         x_189 += out_115;  out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:50:31.9905923Z         
2025-03-14T04:50:31.9906211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:50:31.9906359Z         out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:50:31.9906432Z         
2025-03-14T04:50:31.9906878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:50:31.9907048Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:50:31.9907117Z         
2025-03-14T04:50:31.9907469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:31.9907613Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:50:31.9908097Z         
2025-03-14T04:50:31.9908562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:50:31.9908742Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:50:31.9908805Z         
2025-03-14T04:50:31.9909106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:31.9909246Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:50:31.9909321Z         
2025-03-14T04:50:31.9909720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:50:31.9909929Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:50:31.9910028Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:50:31.9910162Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:50:31.9910227Z         
2025-03-14T04:50:31.9910573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:50:31.9910703Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:50:31.9910777Z         
2025-03-14T04:50:31.9911114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:50:31.9911246Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:50:31.9911309Z         
2025-03-14T04:50:31.9911712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:50:31.9911930Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:50:31.9912001Z         
2025-03-14T04:50:31.9912450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:50:31.9912577Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:50:31.9913026Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:50:31.9913152Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:50:31.9913279Z         x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:50:31.9913342Z         
2025-03-14T04:50:31.9913658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:50:31.9913785Z         tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32);  x_190 = None
2025-03-14T04:50:31.9913853Z         
2025-03-14T04:50:31.9914144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:31.9914968Z         x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:50:31.9915060Z         
2025-03-14T04:50:31.9915349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:50:31.9915543Z         x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False);  x_191 = None
2025-03-14T04:50:31.9915614Z         
2025-03-14T04:50:31.9916022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:50:31.9916919Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:50:31.9916992Z         
2025-03-14T04:50:31.9917361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:50:31.9918216Z         x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:50:31.9918282Z         
2025-03-14T04:50:31.9918637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:50:31.9918793Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:50:31.9918943Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:50:31.9919006Z         
2025-03-14T04:50:31.9919455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:50:31.9919618Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75);  x_193 = None
2025-03-14T04:50:31.9919799Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:50:31.9919997Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:50:31.9920055Z         
2025-03-14T04:50:31.9920450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:50:31.9920648Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:50:31.9920751Z         
2025-03-14T04:50:31.9921169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:50:31.9921345Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:50:31.9921483Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:50:31.9921624Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:50:31.9921685Z         
2025-03-14T04:50:31.9922052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:50:31.9922218Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:50:31.9922286Z         
2025-03-14T04:50:31.9922591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:50:31.9922734Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:50:31.9922793Z         
2025-03-14T04:50:31.9923102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:50:31.9923226Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:50:31.9923352Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:50:31.9923491Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:50:31.9923557Z         
2025-03-14T04:50:31.9923867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:50:31.9923992Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:50:31.9924106Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:50:31.9924253Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:50:31.9924313Z         
2025-03-14T04:50:31.9924618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:50:31.9924732Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:50:31.9924824Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:50:31.9924940Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:50:31.9925009Z         
2025-03-14T04:50:31.9925309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:50:31.9925456Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:50:31.9925541Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:50:31.9925669Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:50:31.9925727Z         
2025-03-14T04:50:31.9926087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:50:31.9926239Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:50:31.9926361Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:50:31.9926422Z         
2025-03-14T04:50:31.9926756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:50:31.9926917Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:50:31.9927055Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:50:31.9927127Z         
2025-03-14T04:50:31.9927430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:50:31.9927591Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:50:31.9927702Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:50:31.9927785Z         
2025-03-14T04:50:31.9928087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:50:31.9928278Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:50:31.9928392Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:50:31.9928460Z         
2025-03-14T04:50:31.9928805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:50:31.9928952Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:50:31.9929014Z         
2025-03-14T04:50:31.9929361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:50:31.9929501Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:50:31.9929572Z         
2025-03-14T04:50:31.9929939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:50:31.9930093Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:50:31.9930227Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:50:31.9930387Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:50:31.9930529Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:50:31.9930599Z         
2025-03-14T04:50:31.9930951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:50:31.9931099Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:50:31.9931220Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:50:31.9931385Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:50:31.9931593Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:50:31.9931674Z         
2025-03-14T04:50:31.9932030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:50:31.9932161Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:50:31.9932327Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:50:31.9932509Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:50:31.9932576Z         
2025-03-14T04:50:31.9932929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:50:31.9933073Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:50:31.9933242Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:50:31.9933366Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:50:31.9933434Z         
2025-03-14T04:50:31.9933735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:50:31.9933833Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:50:31.9933957Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:50:31.9934016Z         
2025-03-14T04:50:31.9934320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:50:31.9934413Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:50:31.9934528Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:50:31.9934591Z         
2025-03-14T04:50:31.9934900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:50:31.9935014Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:50:31.9935143Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:50:31.9935204Z         
2025-03-14T04:50:31.9935515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:50:31.9935623Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:50:31.9935752Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:50:31.9935815Z         
2025-03-14T04:50:31.9936163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:50:31.9936338Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:50:31.9936408Z         
2025-03-14T04:50:31.9936782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:50:31.9936951Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:50:31.9937011Z         
2025-03-14T04:50:31.9937400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:50:31.9937574Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:50:31.9937642Z         
2025-03-14T04:50:31.9938121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:50:31.9938258Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:50:31.9938319Z         
2025-03-14T04:50:31.9938651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:31.9938793Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:50:31.9938898Z         
2025-03-14T04:50:31.9939340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:50:31.9939461Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:50:31.9939566Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:50:31.9939690Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:50:31.9939751Z         
2025-03-14T04:50:31.9940238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:50:31.9940398Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:50:31.9940645Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:50:31.9940707Z         
2025-03-14T04:50:31.9941171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:50:31.9941339Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:50:31.9941399Z         
2025-03-14T04:50:31.9941697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:31.9941846Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:50:31.9941915Z         
2025-03-14T04:50:31.9942291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:50:31.9942443Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:50:31.9942504Z         
2025-03-14T04:50:31.9942804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:50:31.9942942Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:50:31.9943011Z         
2025-03-14T04:50:31.9943389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:50:31.9943532Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:50:31.9943596Z         
2025-03-14T04:50:31.9944079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:50:31.9944211Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:50:31.9944337Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:50:31.9944487Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:50:31.9944619Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:50:31.9944680Z         
2025-03-14T04:50:31.9945082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:50:31.9945225Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:50:31.9945297Z         
2025-03-14T04:50:48.5066461Z 
2025-03-14T04:50:48.5070873Z class GraphModule(torch.nn.Module):
2025-03-14T04:50:48.5072473Z     def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:50:48.5077767Z         l_features_res4_ = L_features_res4_
2025-03-14T04:50:48.5078907Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:50:48.5079487Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:50:48.5080036Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:50:48.5080580Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:50:48.5081181Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:50:48.5081758Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:50:48.5082318Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:50:48.5082685Z         
2025-03-14T04:50:48.5083280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:50:48.5083960Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:50:48.5084238Z         
2025-03-14T04:50:48.5084639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:48.5085145Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:50:48.5085407Z         
2025-03-14T04:50:48.5085952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:50:48.5086616Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:50:48.5086887Z         
2025-03-14T04:50:48.5087286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:48.5087786Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:50:48.5088050Z         
2025-03-14T04:50:48.5089036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:50:48.5089664Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:50:48.5090111Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:50:48.5090389Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:50:48.5090627Z         
2025-03-14T04:50:48.5091049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:50:48.5091682Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:50:48.5091940Z         
2025-03-14T04:50:48.5092363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:50:48.5092893Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:50:48.5093142Z         
2025-03-14T04:50:48.5093632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:50:48.5094308Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:50:48.5094655Z         
2025-03-14T04:50:48.5095193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:50:48.5095809Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:50:48.5096316Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:50:48.5096831Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:50:48.5097129Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:50:48.5097366Z         
2025-03-14T04:50:48.5097768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:50:48.5098257Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:50:48.5098505Z         
2025-03-14T04:50:48.5098859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:48.5099837Z         x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:50:48.5100570Z         
2025-03-14T04:50:48.5100932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:50:48.5101445Z         x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:50:48.5101746Z         
2025-03-14T04:50:48.5102216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:50:48.5103359Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:50:48.5104156Z         
2025-03-14T04:50:48.5104605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:50:48.5105631Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:50:48.5106345Z         
2025-03-14T04:50:48.5106783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:50:48.5107357Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:50:48.5107721Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:50:48.5107991Z         
2025-03-14T04:50:48.5108524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:50:48.5109154Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T04:50:48.5109540Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:50:48.5109950Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:50:48.5110244Z         
2025-03-14T04:50:48.5110736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:50:48.5111466Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:50:48.5111785Z         
2025-03-14T04:50:48.5112307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:50:48.5112947Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:50:48.5113312Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:50:48.5113666Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:50:48.5113940Z         
2025-03-14T04:50:48.5114421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:50:48.5115047Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:50:48.5115348Z         
2025-03-14T04:50:48.5115761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:50:48.5116286Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:50:48.5116558Z         
2025-03-14T04:50:48.5117008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:50:48.5117534Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:50:48.5117862Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:50:48.5118224Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:50:48.5118492Z         
2025-03-14T04:50:48.5118901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:50:48.5119433Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:50:48.5119742Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:50:48.5120075Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:50:48.5120345Z         
2025-03-14T04:50:48.5120750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:50:48.5121260Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:50:48.5121536Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:50:48.5121809Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:50:48.5122071Z         
2025-03-14T04:50:48.5122477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:50:48.5123003Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:50:48.5123298Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:50:48.5123572Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:50:48.5123825Z         
2025-03-14T04:50:48.5124272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:50:48.5124801Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:50:48.5125135Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:50:48.5125375Z         
2025-03-14T04:50:48.5125768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:50:48.5126290Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:50:48.5126617Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:50:48.5126883Z         
2025-03-14T04:50:48.5127267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:50:48.5127770Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:50:48.5128096Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:50:48.5128325Z         
2025-03-14T04:50:48.5128716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:50:48.5129261Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:50:48.5129618Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:50:48.5129855Z         
2025-03-14T04:50:48.5130321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:50:48.5130863Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:50:48.5131130Z         
2025-03-14T04:50:48.5131704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:50:48.5132285Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:50:48.5132568Z         
2025-03-14T04:50:48.5133056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:50:48.5133591Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:50:48.5133908Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:50:48.5134242Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:50:48.5134590Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:50:48.5134852Z         
2025-03-14T04:50:48.5135281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:50:48.5135813Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:50:48.5136126Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:50:48.5136450Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:50:48.5136792Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:50:48.5137045Z         
2025-03-14T04:50:48.5137457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:50:48.5137956Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:50:48.5138280Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:50:48.5138626Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:50:48.5138879Z         
2025-03-14T04:50:48.5139325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:50:48.5139838Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:50:48.5140173Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:50:48.5140522Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:50:48.5140770Z         
2025-03-14T04:50:48.5141159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:50:48.5141613Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:50:48.5141871Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:50:48.5142103Z         
2025-03-14T04:50:48.5142489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:50:48.5142936Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:50:48.5143194Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:50:48.5143423Z         
2025-03-14T04:50:48.5143846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:50:48.5144319Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:50:48.5144645Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:50:48.5144903Z         
2025-03-14T04:50:48.5145294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:50:48.5145773Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:50:48.5146062Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:50:48.5146312Z         
2025-03-14T04:50:48.5146750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:50:48.5147331Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:50:48.5147633Z         
2025-03-14T04:50:48.5148049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:50:48.5148598Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:50:48.5148885Z         
2025-03-14T04:50:48.5149358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:50:48.5149968Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:50:48.5150262Z         
2025-03-14T04:50:48.5150835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:50:48.5151518Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:50:48.5151775Z         
2025-03-14T04:50:48.5152161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:48.5152657Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:50:48.5152919Z         
2025-03-14T04:50:48.5153439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:50:48.5154051Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:50:48.5154324Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:50:48.5154600Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:50:48.5154842Z         
2025-03-14T04:50:48.5155398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:50:48.5156083Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:50:48.5156541Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:50:48.5156895Z         
2025-03-14T04:50:48.5157480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:50:48.5158150Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:50:48.5158463Z         
2025-03-14T04:50:48.5158841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:48.5159338Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:50:48.5159601Z         
2025-03-14T04:50:48.5160065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:50:48.5160971Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:50:48.5161256Z         
2025-03-14T04:50:48.5161650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:50:48.5162168Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:50:48.5162430Z         
2025-03-14T04:50:48.5162894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:50:48.5163464Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:50:48.5163720Z         
2025-03-14T04:50:48.5164289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:50:48.5164955Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:50:48.5165260Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:50:48.5165586Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:50:48.5165917Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:50:48.5166167Z         
2025-03-14T04:50:48.5166618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:50:48.5167156Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:50:48.5167397Z         
2025-03-14T04:50:48.5167545Z 
2025-03-14T04:50:48.5167641Z class GraphModule(torch.nn.Module):
2025-03-14T04:50:48.5169012Z     def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:50:48.5170371Z         l_features_res4_ = L_features_res4_
2025-03-14T04:50:48.5170805Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:50:48.5171553Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:50:48.5172111Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:50:48.5172788Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:50:48.5173433Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:50:48.5174026Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:50:48.5174599Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:50:48.5174976Z         
2025-03-14T04:50:48.5175539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:50:48.5176217Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:50:48.5176507Z         
2025-03-14T04:50:48.5176911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:48.5177424Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:50:48.5177689Z         
2025-03-14T04:50:48.5178231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:50:48.5178900Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:50:48.5179179Z         
2025-03-14T04:50:48.5179572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:48.5180077Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:50:48.5180342Z         
2025-03-14T04:50:48.5180813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:50:48.5181444Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:50:48.5181785Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:50:48.5182060Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:50:48.5182305Z         
2025-03-14T04:50:48.5182738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:50:48.5183268Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:50:48.5183515Z         
2025-03-14T04:50:48.5183940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:50:48.5184458Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:50:48.5184706Z         
2025-03-14T04:50:48.5185193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:50:48.5185847Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:50:48.5186221Z         
2025-03-14T04:50:48.5186738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:50:48.5187387Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:50:48.5187912Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:50:48.5188413Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:50:48.5188718Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:50:48.5188962Z         
2025-03-14T04:50:48.5189368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:50:48.5189859Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:50:48.5190112Z         
2025-03-14T04:50:48.5190465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:50:48.5191420Z         x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:50:48.5192173Z         
2025-03-14T04:50:48.5192551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:50:48.5193087Z         x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:50:48.5193404Z         
2025-03-14T04:50:48.5193893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:50:48.5195009Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:50:48.5195800Z         
2025-03-14T04:50:48.5196260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:50:48.5197328Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:50:48.5198052Z         
2025-03-14T04:50:48.5198474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:50:48.5199037Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:50:48.5199399Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:50:48.5199667Z         
2025-03-14T04:50:48.5200231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:50:48.5200877Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T04:50:48.5201317Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:50:48.5201736Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:50:48.5202041Z         
2025-03-14T04:50:48.5202556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:50:48.5203252Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:50:48.5203590Z         
2025-03-14T04:50:48.5204140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:50:48.5204810Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:50:48.5205178Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:50:48.5205537Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:50:48.5205803Z         
2025-03-14T04:50:48.5206292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:50:48.5206918Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:50:48.5207232Z         
2025-03-14T04:50:48.5207697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:50:48.5208281Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:50:48.5208566Z         
2025-03-14T04:50:48.5209006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:50:48.5209576Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:50:48.5209917Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:50:48.5210272Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:50:48.5210565Z         
2025-03-14T04:50:48.5211017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:50:48.5211632Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:50:48.5211985Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:50:48.5212355Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:50:48.5212660Z         
2025-03-14T04:50:48.5213120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:50:48.5213662Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:50:48.5213957Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:50:48.5214249Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:50:48.5214568Z         
2025-03-14T04:50:48.5215015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:50:48.5215618Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:50:48.5215943Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:50:48.5216241Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:50:48.5216511Z         
2025-03-14T04:50:48.5216972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:50:48.5217519Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:50:48.5217877Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:50:48.5218132Z         
2025-03-14T04:50:48.5218564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:50:48.5219123Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:50:48.5219484Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:50:48.5219738Z         
2025-03-14T04:50:48.5220162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:50:48.5220717Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:50:48.5221071Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:50:48.5221328Z         
2025-03-14T04:50:48.5221754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:50:48.5222343Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:50:48.5222731Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:50:48.5222987Z         
2025-03-14T04:50:48.5223446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:50:48.5224025Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:50:48.5224306Z         
2025-03-14T04:50:48.5224758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:50:48.5225357Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:50:48.5225638Z         
2025-03-14T04:50:48.5226108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:50:48.5226703Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:50:48.5227054Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:50:48.5227421Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:50:48.5227808Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:50:48.5228096Z         
2025-03-14T04:50:48.5228573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:50:48.5229250Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:50:48.5229612Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:50:48.5230014Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:50:48.5230402Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:50:48.5230688Z         
2025-03-14T04:50:48.5231160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:50:48.5231716Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:50:48.5232078Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:50:48.5232466Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:50:48.5232747Z         
2025-03-14T04:50:48.5233213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:50:48.5233771Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:50:48.5234120Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:50:48.5234492Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:50:48.5234760Z         
2025-03-14T04:50:48.5235180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:50:48.5235661Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:50:48.5235934Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:50:48.5236178Z         
2025-03-14T04:50:48.5236591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:50:48.5237073Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:50:48.5237343Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:50:48.5237587Z         
2025-03-14T04:50:48.5237995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:50:48.5238489Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:50:48.5238795Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:50:48.5239052Z         
2025-03-14T04:50:48.5239465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:50:48.5239946Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:50:48.5240234Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:50:48.5240491Z         
2025-03-14T04:50:48.5240936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:50:48.5241547Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:50:48.5241846Z         
2025-03-14T04:50:48.5242279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:50:48.5242908Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:50:48.5243207Z         
2025-03-14T04:50:48.5243697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:50:48.5244363Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:50:48.5244662Z         
2025-03-14T04:50:48.5245255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:50:48.5245994Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:50:48.5246271Z         
2025-03-14T04:50:48.5246695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:48.5247251Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:50:48.5247551Z         
2025-03-14T04:50:48.5248148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:50:48.5248835Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:50:48.5249144Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:50:48.5249453Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:50:48.5249714Z         
2025-03-14T04:50:48.5250348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:50:48.5251125Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:50:48.5251727Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:50:48.5252141Z         
2025-03-14T04:50:48.5252785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:50:48.5253579Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:50:48.5253907Z         
2025-03-14T04:50:48.5254346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:48.5254921Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:50:48.5255228Z         
2025-03-14T04:50:48.5255767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:50:48.5256429Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:50:48.5256731Z         
2025-03-14T04:50:48.5257166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:50:48.5257727Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:50:48.5258025Z         
2025-03-14T04:50:48.5258603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:50:48.5259250Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:50:48.5259583Z         
2025-03-14T04:50:48.5260233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:50:48.5261139Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:50:48.5261503Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:50:48.5261875Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:50:48.5262264Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:50:48.5262555Z         
2025-03-14T04:50:48.5263086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:50:48.5263691Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:50:48.5263949Z         
2025-03-14T04:50:50.8734437Z 
2025-03-14T04:50:50.8739718Z class GraphModule(torch.nn.Module):
2025-03-14T04:50:50.8744606Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"):
2025-03-14T04:50:50.8749921Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T04:50:50.8754402Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T04:50:50.8759263Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T04:50:50.8763881Z         
2025-03-14T04:50:50.8766335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:50:50.8767065Z         pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T04:50:50.8767455Z         
2025-03-14T04:50:50.8768015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:50:50.8768712Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T04:50:50.8769113Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:50:50.8769466Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:50:50.8769734Z         
2025-03-14T04:50:50.8770219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:50:50.8770826Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T04:50:50.8771113Z         
2025-03-14T04:50:50.8771625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:50:50.8772160Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:50:50.8772425Z         
2025-03-14T04:50:50.8772846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:50:50.8773719Z         getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:50:50.8774044Z         getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:50:50.8774381Z         widths: "f32[328500][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T04:50:50.8774764Z         
2025-03-14T04:50:50.8775184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:50:50.8775710Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:50:50.8776024Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:50:50.8776365Z         heights: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:50:50.8776646Z         
2025-03-14T04:50:50.8777068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:50:50.8777578Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:50:50.8777853Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:50:50.8778129Z         ctr_x: "f32[328500][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T04:50:50.8778378Z         
2025-03-14T04:50:50.8778795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:50:50.8779335Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:50:50.8779637Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:50:50.8779920Z         ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T04:50:50.8780173Z         
2025-03-14T04:50:50.8780644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:50:50.8781174Z         getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:50:50.8781518Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T04:50:50.8781751Z         
2025-03-14T04:50:50.8782148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:50:50.8782702Z         getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:50:50.8783024Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T04:50:50.8783288Z         
2025-03-14T04:50:50.8783683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:50:50.8784200Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:50:50.8784524Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:50:50.8784760Z         
2025-03-14T04:50:50.8785152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:50:50.8785704Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:50:50.8786053Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:50:50.8786285Z         
2025-03-14T04:50:50.8786721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:50:50.8787322Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:50:50.8787588Z         
2025-03-14T04:50:50.8787996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:50:50.8788572Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:50:50.8788822Z         
2025-03-14T04:50:50.8789254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:50:50.8789795Z         getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:50:50.8790115Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T04:50:50.8790452Z         getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:50:50.8790808Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T04:50:50.8791064Z         
2025-03-14T04:50:50.8791495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:50:50.8792039Z         getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:50:50.8792355Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T04:50:50.8792684Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:50:50.8793028Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T04:50:50.8793285Z         
2025-03-14T04:50:50.8793700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:50:50.8794196Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:50:50.8794523Z         getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:50:50.8794870Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T04:50:50.8795121Z         
2025-03-14T04:50:50.8795541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:50:50.8796038Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:50:50.8796373Z         getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:50:50.8796726Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T04:50:50.8796983Z         
2025-03-14T04:50:50.8797382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:50:50.8797846Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:50:50.8798111Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:50:50.8798348Z         
2025-03-14T04:50:50.8798743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:50:50.8799193Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:50:50.8799450Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:50:50.8799684Z         
2025-03-14T04:50:50.8800108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:50:50.8800581Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:50:50.8800871Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:50:50.8801153Z         
2025-03-14T04:50:50.8801539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:50:50.8802004Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:50:50.8802290Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:50:50.8802537Z         
2025-03-14T04:50:50.8802964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:50:50.8803542Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:50:50.8803833Z         
2025-03-14T04:50:50.8804235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:50:50.8804782Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:50:50.8805058Z         
2025-03-14T04:50:50.8805524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:50:50.8806127Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:50:50.8806410Z         
2025-03-14T04:50:50.8806976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:50:50.8807652Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:50:50.8807899Z         
2025-03-14T04:50:50.8808279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:50.8808754Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:50:50.8809000Z         
2025-03-14T04:50:50.8809521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:50:50.8810178Z         topk = l_pred_objectness_logits_0_.topk(6000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T04:50:50.8810508Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:50:50.8810774Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:50:50.8810998Z         
2025-03-14T04:50:50.8811631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:50:50.8812337Z         getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:50:50.8812800Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)];  proposals_i_1 = getitem_18 = topk_idx = None
2025-03-14T04:50:50.8813152Z         
2025-03-14T04:50:50.8813754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:50:50.8814446Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:50:50.8814728Z         
2025-03-14T04:50:50.8815162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:50:50.8815667Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:50:50.8815936Z         
2025-03-14T04:50:50.8816412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:50:50.8817004Z         getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:50:50.8817273Z         
2025-03-14T04:50:50.8817658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:50:50.8818155Z         tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32);  getitem_20 = None
2025-03-14T04:50:50.8818427Z         
2025-03-14T04:50:50.8818872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:50:50.8819425Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:50:50.8819681Z         
2025-03-14T04:50:50.8820255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:50:50.8820919Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T04:50:50.8821222Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:50:50.8821541Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:50:50.8821876Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:50:50.8822133Z         
2025-03-14T04:50:50.8822594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:50:50.8823129Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:50:50.8823363Z         
2025-03-14T04:51:14.4911075Z 
2025-03-14T04:51:14.4911981Z class GraphModule(torch.nn.Module):
2025-03-14T04:51:14.4913599Z     def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"):
2025-03-14T04:51:14.4915052Z         l_stack0_ = L_stack0_
2025-03-14T04:51:14.4915458Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:51:14.4916048Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:51:14.4917776Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:51:14.4918459Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:51:14.4919071Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:51:14.4919495Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:51:14.4919904Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:51:14.4920311Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:51:14.4920615Z         
2025-03-14T04:51:14.4921200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T04:51:14.4921870Z         mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T04:51:14.4922142Z         
2025-03-14T04:51:14.4922656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:51:14.4923691Z         scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:51:14.4924444Z         
2025-03-14T04:51:14.4924887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:51:14.4925922Z         proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:51:14.4926665Z         
2025-03-14T04:51:14.4927038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:51:14.4927503Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:51:14.4927754Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:51:14.4927982Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:51:14.4928250Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:51:14.4928509Z         getitem_2: "Sym(1261 - s0)" = size_1[0]
2025-03-14T04:51:14.4928767Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:51:14.4928988Z         
2025-03-14T04:51:14.4929375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:51:14.4930372Z         proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:51:14.4931114Z         
2025-03-14T04:51:14.4931791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:51:14.4932481Z         deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:51:14.4932791Z         
2025-03-14T04:51:14.4933205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:51:14.4933761Z         boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:51:14.4934024Z         
2025-03-14T04:51:14.4934417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:51:14.4934911Z         getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:51:14.4935215Z         getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:51:14.4935551Z         widths: "f32[3261][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:51:14.4935822Z         
2025-03-14T04:51:14.4936243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:51:14.4936759Z         getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:51:14.4937055Z         getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:51:14.4937382Z         heights: "f32[3261][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:51:14.4937653Z         
2025-03-14T04:51:14.4938068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:51:14.4938593Z         getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:51:14.4938871Z         mul: "f32[3261][1]cpu" = 0.5 * widths
2025-03-14T04:51:14.4939148Z         ctr_x: "f32[3261][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:51:14.4939396Z         
2025-03-14T04:51:14.4939833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:51:14.4940387Z         getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:51:14.4940680Z         mul_1: "f32[3261][1]cpu" = 0.5 * heights
2025-03-14T04:51:14.4940994Z         ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:51:14.4941241Z         
2025-03-14T04:51:14.4941696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:51:14.4942231Z         getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:51:14.4942575Z         dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:51:14.4942832Z         
2025-03-14T04:51:14.4943273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:51:14.4943932Z         getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:51:14.4944293Z         dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:51:14.4944551Z         
2025-03-14T04:51:14.4945145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:51:14.4945674Z         getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:51:14.4946007Z         dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:51:14.4946244Z         
2025-03-14T04:51:14.4946705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:51:14.4947274Z         getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:51:14.4947680Z         dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:51:14.4947918Z         
2025-03-14T04:51:14.4948359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:51:14.4948914Z         dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:51:14.4949185Z         
2025-03-14T04:51:14.4949622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:51:14.4950385Z         dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:51:14.4950659Z         
2025-03-14T04:51:14.4951117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:51:14.4951694Z         getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:51:14.4952028Z         mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:51:14.4952375Z         getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:51:14.4952734Z         pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:51:14.4952999Z         
2025-03-14T04:51:14.4953434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:51:14.4953974Z         getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:51:14.4954285Z         mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:51:14.4954614Z         getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:51:14.4954949Z         pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:51:14.4955203Z         
2025-03-14T04:51:14.4955622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:51:14.4956180Z         exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:51:14.4956559Z         getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:51:14.4956902Z         pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:51:14.4957148Z         
2025-03-14T04:51:14.4957552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:51:14.4958068Z         exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:51:14.4958411Z         getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:51:14.4958856Z         pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:51:14.4959212Z         
2025-03-14T04:51:14.4959647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:51:14.4960124Z         mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:51:14.4960464Z         x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:51:14.4960959Z         
2025-03-14T04:51:14.4961417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:51:14.4961989Z         mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:51:14.4962246Z         y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:51:14.4962476Z         
2025-03-14T04:51:14.4962872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:51:14.4963363Z         mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:51:14.4963647Z         x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:51:14.4963893Z         
2025-03-14T04:51:14.4964281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:51:14.4964743Z         mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:51:14.4965028Z         y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:51:14.4965268Z         
2025-03-14T04:51:14.4965701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:51:14.4966281Z         pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:51:14.4966571Z         
2025-03-14T04:51:14.4966989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:51:14.4967539Z         predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320));  pred_boxes = None
2025-03-14T04:51:14.4967824Z         
2025-03-14T04:51:14.4968264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:51:14.4968887Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:51:14.4969269Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:51:14.4969556Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:51:14.4969862Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:51:14.4970194Z         getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:51:14.4970462Z         
2025-03-14T04:51:14.4970855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:51:14.4971428Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:51:14.4971895Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:51:14.4972170Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:51:14.4972573Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:51:14.4972935Z         getitem_26: "Sym(1261 - s0)" = size_3[0]
2025-03-14T04:51:14.4973179Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:51:14.4973402Z         
2025-03-14T04:51:14.4973833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:51:14.4974471Z         probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:51:14.4974767Z         
2025-03-14T04:51:14.4975220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:51:14.4975871Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:51:14.4976236Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:51:14.4976533Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:51:14.4976837Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:51:14.4977154Z         getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:51:14.4977419Z         
2025-03-14T04:51:14.4977983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:51:14.4978699Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:51:14.4979046Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:51:14.4979385Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:51:14.4979727Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:51:14.4980020Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:51:14.4980262Z         
2025-03-14T04:51:14.4980707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:51:14.4981240Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:51:14.4981473Z         
2025-03-14T04:51:14.4981614Z 
2025-03-14T04:51:14.4981711Z class GraphModule(torch.nn.Module):
2025-03-14T04:51:14.4983138Z     def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"):
2025-03-14T04:51:14.4984484Z         l_stack0_ = L_stack0_
2025-03-14T04:51:14.4984862Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:51:14.4985425Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:51:14.4985981Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:51:14.4986537Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:51:14.4987015Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:51:14.4987411Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:51:14.4987831Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:51:14.4988221Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:51:14.4988537Z         
2025-03-14T04:51:14.4989079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T04:51:14.4989716Z         mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T04:51:14.4989983Z         
2025-03-14T04:51:14.4990382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:51:14.4991365Z         scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:51:14.4992094Z         
2025-03-14T04:51:14.4992503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:51:14.4993532Z         proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:51:14.4994316Z         
2025-03-14T04:51:14.4994694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:51:14.4995163Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:51:14.4995432Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:51:14.4995707Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:51:14.4996002Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:51:14.4996282Z         getitem_2: "Sym(1261 - s0)" = size_1[0]
2025-03-14T04:51:14.4996542Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:51:14.4996774Z         
2025-03-14T04:51:14.4997168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:51:14.4998163Z         proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:51:14.4998927Z         
2025-03-14T04:51:14.4999409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:51:14.5000018Z         deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:51:14.5000293Z         
2025-03-14T04:51:14.5000712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:51:14.5001266Z         boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:51:14.5001561Z         
2025-03-14T04:51:14.5002012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:51:14.5002529Z         getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:51:14.5002843Z         getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:51:14.5003201Z         widths: "f32[3261][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:51:14.5003472Z         
2025-03-14T04:51:14.5003886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:51:14.5004394Z         getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:51:14.5004696Z         getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:51:14.5005019Z         heights: "f32[3261][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:51:14.5005285Z         
2025-03-14T04:51:14.5005694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:51:14.5006198Z         getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:51:14.5006466Z         mul: "f32[3261][1]cpu" = 0.5 * widths
2025-03-14T04:51:14.5006731Z         ctr_x: "f32[3261][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:51:14.5006975Z         
2025-03-14T04:51:14.5007389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:51:14.5007968Z         getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:51:14.5008353Z         mul_1: "f32[3261][1]cpu" = 0.5 * heights
2025-03-14T04:51:14.5008743Z         ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:51:14.5009044Z         
2025-03-14T04:51:14.5009544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:51:14.5010108Z         getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:51:14.5010479Z         dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:51:14.5010738Z         
2025-03-14T04:51:14.5011285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:51:14.5011940Z         getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:51:14.5012306Z         dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:51:14.5012569Z         
2025-03-14T04:51:14.5013001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:51:14.5013535Z         getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:51:14.5013871Z         dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:51:14.5014113Z         
2025-03-14T04:51:14.5014521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:51:14.5015077Z         getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:51:14.5015437Z         dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:51:14.5015677Z         
2025-03-14T04:51:14.5016118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:51:14.5016734Z         dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:51:14.5017006Z         
2025-03-14T04:51:14.5017498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:51:14.5018077Z         dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:51:14.5018337Z         
2025-03-14T04:51:14.5018785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:51:14.5019349Z         getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:51:14.5019675Z         mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:51:14.5020017Z         getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:51:14.5020377Z         pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:51:14.5020630Z         
2025-03-14T04:51:14.5021059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:51:14.5021587Z         getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:51:14.5021894Z         mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:51:14.5022212Z         getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:51:14.5022547Z         pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:51:14.5022797Z         
2025-03-14T04:51:14.5023215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:51:14.5023711Z         exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:51:14.5024035Z         getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:51:14.5024373Z         pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:51:14.5024611Z         
2025-03-14T04:51:14.5025030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:51:14.5025529Z         exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:51:14.5025857Z         getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:51:14.5026205Z         pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:51:14.5026455Z         
2025-03-14T04:51:14.5026853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:51:14.5027314Z         mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:51:14.5027568Z         x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:51:14.5027797Z         
2025-03-14T04:51:14.5028189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:51:14.5028639Z         mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:51:14.5028891Z         y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:51:14.5029119Z         
2025-03-14T04:51:14.5029537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:51:14.5030023Z         mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:51:14.5030481Z         x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:51:14.5030734Z         
2025-03-14T04:51:14.5031135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:51:14.5031600Z         mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:51:14.5031884Z         y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:51:14.5032126Z         
2025-03-14T04:51:14.5032547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:51:14.5033130Z         pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:51:14.5033425Z         
2025-03-14T04:51:14.5033849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:51:14.5034416Z         predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320));  pred_boxes = None
2025-03-14T04:51:14.5034708Z         
2025-03-14T04:51:14.5035144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:51:14.5035747Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:51:14.5036100Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:51:14.5036386Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:51:14.5036679Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:51:14.5036993Z         getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:51:14.5037253Z         
2025-03-14T04:51:14.5037636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:51:14.5038200Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:51:14.5038551Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:51:14.5038796Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:51:14.5039164Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:51:14.5039513Z         getitem_26: "Sym(1261 - s0)" = size_3[0]
2025-03-14T04:51:14.5039760Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:51:14.5039982Z         
2025-03-14T04:51:14.5040406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:51:14.5040974Z         probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:51:14.5041259Z         
2025-03-14T04:51:14.5041703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:51:14.5042313Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:51:14.5042677Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:51:14.5043000Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:51:14.5043303Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:51:14.5043652Z         getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:51:14.5043911Z         
2025-03-14T04:51:14.5044468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:51:14.5045173Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:51:14.5045510Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:51:14.5045846Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:51:14.5046192Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:51:14.5046487Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:51:14.5046725Z         
2025-03-14T04:51:14.5047174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:51:14.5047702Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:51:14.5047937Z         
2025-03-14T04:51:14.5048065Z 
2025-03-14T04:51:14.5048163Z class GraphModule(torch.nn.Module):
2025-03-14T04:51:14.5049572Z     def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"):
2025-03-14T04:51:14.5050936Z         l_stack0_ = L_stack0_
2025-03-14T04:51:14.5051332Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:51:14.5052055Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:51:14.5052695Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:51:14.5053319Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:51:14.5053809Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:51:14.5054256Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:51:14.5054689Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:51:14.5055120Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:51:14.5055438Z         
2025-03-14T04:51:14.5056024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T04:51:14.5056767Z         mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T04:51:14.5057060Z         
2025-03-14T04:51:14.5057501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:51:14.5058647Z         scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:51:14.5059838Z         
2025-03-14T04:51:14.5060293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:51:14.5061557Z         proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:51:14.5062311Z         
2025-03-14T04:51:14.5062677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:51:14.5063130Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:51:14.5063378Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:51:14.5063604Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:51:14.5063874Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:51:14.5064134Z         getitem_2: "Sym(1261 - s0)" = size_1[0]
2025-03-14T04:51:14.5064374Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:51:14.5064591Z         
2025-03-14T04:51:14.5064959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:51:14.5066225Z         proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:51:14.5066941Z         
2025-03-14T04:51:14.5067392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:51:14.5067961Z         deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:51:14.5068225Z         
2025-03-14T04:51:14.5068616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:51:14.5069132Z         boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:51:14.5069412Z         
2025-03-14T04:51:14.5069804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:51:14.5070288Z         getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:51:14.5070589Z         getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:51:14.5070900Z         widths: "f32[3261][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:51:14.5071158Z         
2025-03-14T04:51:14.5071558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:51:14.5072156Z         getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:51:14.5072458Z         getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:51:14.5072834Z         heights: "f32[3261][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:51:14.5073103Z         
2025-03-14T04:51:14.5073506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:51:14.5073994Z         getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:51:14.5074249Z         mul: "f32[3261][1]cpu" = 0.5 * widths
2025-03-14T04:51:14.5074509Z         ctr_x: "f32[3261][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:51:14.5074745Z         
2025-03-14T04:51:14.5075154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:51:14.5075650Z         getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:51:14.5075926Z         mul_1: "f32[3261][1]cpu" = 0.5 * heights
2025-03-14T04:51:14.5076187Z         ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:51:14.5076420Z         
2025-03-14T04:51:14.5076819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:51:14.5077322Z         getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:51:14.5077641Z         dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:51:14.5077872Z         
2025-03-14T04:51:14.5078259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:51:14.5078760Z         getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:51:14.5079081Z         dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:51:14.5079318Z         
2025-03-14T04:51:14.5079713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:51:14.5080206Z         getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:51:14.5080520Z         dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:51:14.5080744Z         
2025-03-14T04:51:14.5081126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:51:14.5081665Z         getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:51:14.5082015Z         dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:51:14.5082258Z         
2025-03-14T04:51:14.5082686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:51:14.5083215Z         dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:51:14.5083472Z         
2025-03-14T04:51:14.5083890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:51:14.5084416Z         dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:51:14.5084668Z         
2025-03-14T04:51:14.5085142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:51:14.5085685Z         getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:51:14.5086042Z         mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:51:14.5086373Z         getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:51:14.5086725Z         pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:51:14.5086992Z         
2025-03-14T04:51:14.5087443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:51:14.5087994Z         getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:51:14.5088315Z         mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:51:14.5088650Z         getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:51:14.5089007Z         pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:51:14.5089269Z         
2025-03-14T04:51:14.5089699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:51:14.5090229Z         exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:51:14.5090574Z         getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:51:14.5090929Z         pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:51:14.5091198Z         
2025-03-14T04:51:14.5091744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:51:14.5092282Z         exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:51:14.5092628Z         getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:51:14.5092997Z         pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:51:14.5093261Z         
2025-03-14T04:51:14.5093682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:51:14.5094165Z         mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:51:14.5094437Z         x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:51:14.5094681Z         
2025-03-14T04:51:14.5095101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:51:14.5095561Z         mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:51:14.5095827Z         y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:51:14.5096064Z         
2025-03-14T04:51:14.5096471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:51:14.5096965Z         mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:51:14.5097263Z         x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:51:14.5097518Z         
2025-03-14T04:51:14.5097921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:51:14.5098467Z         mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:51:14.5098765Z         y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:51:14.5099020Z         
2025-03-14T04:51:14.5099515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:51:14.5100123Z         pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:51:14.5100427Z         
2025-03-14T04:51:14.5100863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:51:14.5101442Z         predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320));  pred_boxes = None
2025-03-14T04:51:14.5101736Z         
2025-03-14T04:51:14.5102200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:51:14.5102841Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:51:14.5103226Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:51:14.5103525Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:51:14.5103839Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:51:14.5104167Z         getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:51:14.5104434Z         
2025-03-14T04:51:14.5104825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:51:14.5105406Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:51:14.5105767Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:51:14.5106014Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:51:14.5106386Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:51:14.5106746Z         getitem_26: "Sym(1261 - s0)" = size_3[0]
2025-03-14T04:51:14.5106997Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:51:14.5107222Z         
2025-03-14T04:51:14.5107656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:51:14.5108233Z         probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:51:14.5108533Z         
2025-03-14T04:51:14.5108995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:51:14.5109626Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:51:14.5109985Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:51:14.5110272Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:51:14.5110567Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:51:14.5110884Z         getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:51:14.5111137Z         
2025-03-14T04:51:14.5111730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:51:14.5112421Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:51:14.5112759Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:51:14.5113135Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:51:14.5113474Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:51:14.5113763Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:51:14.5113996Z         
2025-03-14T04:51:14.5114427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:51:14.5114941Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:51:14.5115169Z         
2025-03-14T04:51:14.5115305Z 
2025-03-14T04:51:14.5115393Z class GraphModule(torch.nn.Module):
2025-03-14T04:51:14.5116768Z     def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"):
2025-03-14T04:51:14.5118120Z         l_stack0_ = L_stack0_
2025-03-14T04:51:14.5118513Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:51:14.5119083Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:51:14.5119656Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:51:14.5120218Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:51:14.5120696Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:51:14.5121092Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:51:14.5121479Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:51:14.5121868Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:51:14.5122149Z         
2025-03-14T04:51:14.5122671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T04:51:14.5123301Z         mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T04:51:14.5123568Z         
2025-03-14T04:51:14.5123961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:51:14.5124985Z         scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:51:14.5125728Z         
2025-03-14T04:51:14.5126142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:51:14.5127212Z         proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:51:14.5127970Z         
2025-03-14T04:51:14.5128344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:51:14.5128808Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:51:14.5129066Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:51:14.5129299Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:51:14.5129570Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:51:14.5129839Z         getitem_2: "Sym(1261 - s0)" = size_1[0]
2025-03-14T04:51:14.5130085Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:51:14.5130304Z         
2025-03-14T04:51:14.5130677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:51:14.5131738Z         proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:51:14.5132483Z         
2025-03-14T04:51:14.5132954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:51:14.5133544Z         deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:51:14.5133822Z         
2025-03-14T04:51:14.5134219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:51:14.5134748Z         boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:51:14.5135028Z         
2025-03-14T04:51:14.5135431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:51:14.5135934Z         getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:51:14.5136241Z         getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:51:14.5136559Z         widths: "f32[3261][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:51:14.5136826Z         
2025-03-14T04:51:14.5137225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:51:14.5137724Z         getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:51:14.5138028Z         getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:51:14.5138361Z         heights: "f32[3261][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:51:14.5138632Z         
2025-03-14T04:51:14.5139032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:51:14.5139571Z         getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:51:14.5139833Z         mul: "f32[3261][1]cpu" = 0.5 * widths
2025-03-14T04:51:14.5140095Z         ctr_x: "f32[3261][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:51:14.5140374Z         
2025-03-14T04:51:14.5140776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:51:14.5141292Z         getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:51:14.5141582Z         mul_1: "f32[3261][1]cpu" = 0.5 * heights
2025-03-14T04:51:14.5141847Z         ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:51:14.5142091Z         
2025-03-14T04:51:14.5142506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:51:14.5143027Z         getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:51:14.5143359Z         dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:51:14.5143600Z         
2025-03-14T04:51:14.5143991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:51:14.5144505Z         getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:51:14.5144833Z         dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:51:14.5145066Z         
2025-03-14T04:51:14.5145453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:51:14.5145967Z         getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:51:14.5146290Z         dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:51:14.5146525Z         
2025-03-14T04:51:14.5146919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:51:14.5147468Z         getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:51:14.5147819Z         dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:51:14.5148049Z         
2025-03-14T04:51:14.5148477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:51:14.5149017Z         dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:51:14.5149276Z         
2025-03-14T04:51:14.5149709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:51:14.5150246Z         dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:51:14.5150501Z         
2025-03-14T04:51:14.5150935Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:51:14.5151484Z         getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:51:14.5151806Z         mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:51:14.5152133Z         getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:51:14.5152506Z         pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:51:14.5152766Z         
2025-03-14T04:51:14.5153191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:51:14.5153752Z         getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:51:14.5154055Z         mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:51:14.5154369Z         getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:51:14.5154702Z         pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:51:14.5154948Z         
2025-03-14T04:51:14.5155352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:51:14.5155851Z         exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:51:14.5156168Z         getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:51:14.5156506Z         pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:51:14.5156750Z         
2025-03-14T04:51:14.5157167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:51:14.5157665Z         exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:51:14.5157986Z         getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:51:14.5158331Z         pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:51:14.5158585Z         
2025-03-14T04:51:14.5158987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:51:14.5159450Z         mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:51:14.5159710Z         x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:51:14.5159943Z         
2025-03-14T04:51:14.5160344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:51:14.5160991Z         mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:51:14.5161254Z         y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:51:14.5161487Z         
2025-03-14T04:51:14.5161877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:51:14.5162361Z         mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:51:14.5162654Z         x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:51:14.5162904Z         
2025-03-14T04:51:14.5163299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:51:14.5163777Z         mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:51:14.5164069Z         y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:51:14.5164309Z         
2025-03-14T04:51:14.5164738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:51:14.5165311Z         pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:51:14.5165680Z         
2025-03-14T04:51:14.5166097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:51:14.5166725Z         predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320));  pred_boxes = None
2025-03-14T04:51:14.5167019Z         
2025-03-14T04:51:14.5167468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:51:14.5168099Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:51:14.5168481Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:51:14.5168786Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:51:14.5169107Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:51:14.5169439Z         getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:51:14.5169738Z         
2025-03-14T04:51:14.5170124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:51:14.5170690Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:51:14.5171040Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:51:14.5171281Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:51:14.5171721Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:51:14.5172113Z         getitem_26: "Sym(1261 - s0)" = size_3[0]
2025-03-14T04:51:14.5172383Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:51:14.5172627Z         
2025-03-14T04:51:14.5173088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:51:14.5173742Z         probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:51:14.5174036Z         
2025-03-14T04:51:14.5174482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:51:14.5175091Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:51:14.5175455Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:51:14.5175743Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:51:14.5176048Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:51:14.5176359Z         getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:51:14.5176616Z         
2025-03-14T04:51:14.5177174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:51:14.5178209Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:51:14.5178598Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:51:14.5178940Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:51:14.5179284Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:51:14.5179628Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:51:14.5179871Z         
2025-03-14T04:51:14.5180318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:51:14.5180878Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:51:14.5181115Z         
2025-03-14T04:51:16.0210170Z 
2025-03-14T04:51:16.0211009Z class GraphModule(torch.nn.Module):
2025-03-14T04:51:16.0212089Z     def forward(self, L_predictions_0_: "f32[3261, 81][81, 1]cpu", L_predictions_1_: "f32[3261, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"):
2025-03-14T04:51:16.0213005Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:51:16.0213252Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:51:16.0213584Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:51:16.0214017Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:51:16.0214429Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:51:16.0214846Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:51:16.0215153Z         
2025-03-14T04:51:16.0215595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:51:16.0216142Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:51:16.0216408Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:51:16.0216655Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:51:16.0216949Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:51:16.0217229Z         getitem_2: "Sym(1261 - s0)" = size_1[0]
2025-03-14T04:51:16.0217489Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:51:16.0217712Z         
2025-03-14T04:51:16.0218111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:51:16.0219128Z         proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:51:16.0219895Z         
2025-03-14T04:51:16.0220396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:51:16.0221011Z         deltas: "f32[3261, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:51:16.0221298Z         
2025-03-14T04:51:16.0221711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:51:16.0222225Z         boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:51:16.0222493Z         
2025-03-14T04:51:16.0222885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:51:16.0223631Z         getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:51:16.0223950Z         getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:51:16.0224625Z         widths: "f32[3261][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:51:16.0224888Z         
2025-03-14T04:51:16.0225293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:51:16.0225885Z         getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:51:16.0226176Z         getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:51:16.0226486Z         heights: "f32[3261][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:51:16.0226750Z         
2025-03-14T04:51:16.0227150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:51:16.0227620Z         getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:51:16.0227871Z         mul: "f32[3261][1]cpu" = 0.5 * widths
2025-03-14T04:51:16.0228119Z         ctr_x: "f32[3261][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:51:16.0228348Z         
2025-03-14T04:51:16.0228734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:51:16.0229217Z         getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:51:16.0229491Z         mul_1: "f32[3261][1]cpu" = 0.5 * heights
2025-03-14T04:51:16.0229741Z         ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:51:16.0229973Z         
2025-03-14T04:51:16.0230399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:51:16.0230892Z         getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:51:16.0231207Z         dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:51:16.0231432Z         
2025-03-14T04:51:16.0231809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:51:16.0232300Z         getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:51:16.0232609Z         dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:51:16.0232834Z         
2025-03-14T04:51:16.0233201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:51:16.0233681Z         getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:51:16.0233989Z         dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:51:16.0234216Z         
2025-03-14T04:51:16.0234589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:51:16.0235111Z         getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:51:16.0235443Z         dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:51:16.0235663Z         
2025-03-14T04:51:16.0236070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:51:16.0236574Z         dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:51:16.0236820Z         
2025-03-14T04:51:16.0237262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:51:16.0237771Z         dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:51:16.0238051Z         
2025-03-14T04:51:16.0238462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:51:16.0238984Z         getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:51:16.0239281Z         mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:51:16.0239605Z         getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:51:16.0239938Z         pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:51:16.0240189Z         
2025-03-14T04:51:16.0240649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:51:16.0241166Z         getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:51:16.0241465Z         mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:51:16.0241775Z         getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:51:16.0242101Z         pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:51:16.0242346Z         
2025-03-14T04:51:16.0242747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:51:16.0243227Z         exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:51:16.0243550Z         getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:51:16.0243879Z         pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:51:16.0244124Z         
2025-03-14T04:51:16.0244527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:51:16.0246083Z         exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:51:16.0246483Z         getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:51:16.0246825Z         pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:51:16.0247068Z         
2025-03-14T04:51:16.0247479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:51:16.0247935Z         mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:51:16.0248196Z         x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:51:16.0248437Z         
2025-03-14T04:51:16.0248836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:51:16.0249285Z         mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:51:16.0249537Z         y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:51:16.0249766Z         
2025-03-14T04:51:16.0250154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:51:16.0250628Z         mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:51:16.0251001Z         x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:51:16.0251261Z         
2025-03-14T04:51:16.0251834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:51:16.0252392Z         mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:51:16.0252690Z         y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:51:16.0252965Z         
2025-03-14T04:51:16.0253397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:51:16.0253977Z         pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:51:16.0254270Z         
2025-03-14T04:51:16.0254691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:51:16.0255248Z         predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320));  pred_boxes = None
2025-03-14T04:51:16.0255539Z         
2025-03-14T04:51:16.0256188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:51:16.0256839Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:51:16.0257200Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:51:16.0257485Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:51:16.0257781Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:51:16.0258097Z         getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:51:16.0258359Z         
2025-03-14T04:51:16.0258738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:51:16.0259301Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:51:16.0259645Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:51:16.0259883Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:51:16.0260244Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:51:16.0260779Z         getitem_26: "Sym(1261 - s0)" = size_3[0]
2025-03-14T04:51:16.0261038Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:51:16.0261258Z         
2025-03-14T04:51:16.0261674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:51:16.0262268Z         probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:51:16.0262596Z         
2025-03-14T04:51:16.0263170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:51:16.0263780Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:51:16.0264138Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:51:16.0264428Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:51:16.0264726Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:51:16.0265130Z         getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:51:16.0265394Z         
2025-03-14T04:51:16.0265952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:51:16.0266720Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:51:16.0267059Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:51:16.0267399Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:51:16.0267738Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:51:16.0268026Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:51:16.0268292Z         
2025-03-14T04:51:16.0268736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:51:16.0269255Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:51:16.0269486Z         
2025-03-14T04:51:16.0269628Z 
2025-03-14T04:51:16.0269723Z class GraphModule(torch.nn.Module):
2025-03-14T04:51:16.0270516Z     def forward(self, L_predictions_0_: "f32[3261, 81][81, 1]cpu", L_predictions_1_: "f32[3261, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"):
2025-03-14T04:51:16.0271286Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:51:16.0271511Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:51:16.0271814Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:51:16.0272204Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:51:16.0272584Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:51:16.0272959Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:51:16.0273236Z         
2025-03-14T04:51:16.0273608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:51:16.0274063Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:51:16.0274300Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:51:16.0274513Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:51:16.0274778Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:51:16.0275032Z         getitem_2: "Sym(1261 - s0)" = size_1[0]
2025-03-14T04:51:16.0275265Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:51:16.0275480Z         
2025-03-14T04:51:16.0275841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:51:16.0276754Z         proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:51:16.0277449Z         
2025-03-14T04:51:16.0277930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:51:16.0278525Z         deltas: "f32[3261, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:51:16.0278793Z         
2025-03-14T04:51:16.0279211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:51:16.0279721Z         boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:51:16.0279990Z         
2025-03-14T04:51:16.0280375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:51:16.0280854Z         getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:51:16.0281140Z         getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:51:16.0281448Z         widths: "f32[3261][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:51:16.0281700Z         
2025-03-14T04:51:16.0282093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:51:16.0282570Z         getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:51:16.0282855Z         getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:51:16.0283158Z         heights: "f32[3261][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:51:16.0283411Z         
2025-03-14T04:51:16.0283790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:51:16.0284253Z         getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:51:16.0284502Z         mul: "f32[3261][1]cpu" = 0.5 * widths
2025-03-14T04:51:16.0284749Z         ctr_x: "f32[3261][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:51:16.0284979Z         
2025-03-14T04:51:16.0285372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:51:16.0285875Z         getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:51:16.0286159Z         mul_1: "f32[3261][1]cpu" = 0.5 * heights
2025-03-14T04:51:16.0286417Z         ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:51:16.0286659Z         
2025-03-14T04:51:16.0287081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:51:16.0287605Z         getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:51:16.0289126Z         dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:51:16.0289383Z         
2025-03-14T04:51:16.0289793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:51:16.0290309Z         getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:51:16.0290635Z         dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:51:16.0290868Z         
2025-03-14T04:51:16.0291258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:51:16.0291830Z         getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:51:16.0292184Z         dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:51:16.0292504Z         
2025-03-14T04:51:16.0292931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:51:16.0293543Z         getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:51:16.0293902Z         dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:51:16.0294125Z         
2025-03-14T04:51:16.0294544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:51:16.0295072Z         dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:51:16.0295324Z         
2025-03-14T04:51:16.0295737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:51:16.0296256Z         dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:51:16.0296506Z         
2025-03-14T04:51:16.0296928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:51:16.0297458Z         getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:51:16.0297764Z         mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:51:16.0298088Z         getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:51:16.0298428Z         pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:51:16.0298679Z         
2025-03-14T04:51:16.0299107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:51:16.0299638Z         getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:51:16.0299953Z         mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:51:16.0300269Z         getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:51:16.0300607Z         pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:51:16.0300857Z         
2025-03-14T04:51:16.0301270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:51:16.0301767Z         exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:51:16.0302086Z         getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:51:16.0302420Z         pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:51:16.0302670Z         
2025-03-14T04:51:16.0303085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:51:16.0303581Z         exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:51:16.0303904Z         getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:51:16.0304254Z         pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:51:16.0304503Z         
2025-03-14T04:51:16.0304896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:51:16.0306246Z         mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:51:16.0306524Z         x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:51:16.0306804Z         
2025-03-14T04:51:16.0307194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:51:16.0307669Z         mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:51:16.0307933Z         y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:51:16.0308169Z         
2025-03-14T04:51:16.0308565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:51:16.0309041Z         mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:51:16.0309342Z         x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:51:16.0309593Z         
2025-03-14T04:51:16.0309977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:51:16.0310457Z         mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:51:16.0310742Z         y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:51:16.0310992Z         
2025-03-14T04:51:16.0311431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:51:16.0312015Z         pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:51:16.0312307Z         
2025-03-14T04:51:16.0312733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:51:16.0313294Z         predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320));  pred_boxes = None
2025-03-14T04:51:16.0313586Z         
2025-03-14T04:51:16.0314035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:51:16.0314658Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:51:16.0315025Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:51:16.0315312Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:51:16.0315619Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:51:16.0315938Z         getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:51:16.0316199Z         
2025-03-14T04:51:16.0316586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:51:16.0317156Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:51:16.0317507Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:51:16.0317754Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:51:16.0318122Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:51:16.0318478Z         getitem_26: "Sym(1261 - s0)" = size_3[0]
2025-03-14T04:51:16.0318725Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:51:16.0318943Z         
2025-03-14T04:51:16.0319423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:51:16.0320036Z         probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:51:16.0320402Z         
2025-03-14T04:51:16.0320844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:51:16.0321451Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:51:16.0321810Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:51:16.0322113Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:51:16.0322400Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:51:16.0322709Z         getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:51:16.0322969Z         
2025-03-14T04:51:16.0323511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:51:16.0324205Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:51:16.0324539Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:51:16.0324868Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:51:16.0325203Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:51:16.0325490Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:51:16.0325725Z         
2025-03-14T04:51:16.0326161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:51:16.0326683Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:51:16.0326920Z         
2025-03-14T04:51:16.0327056Z 
2025-03-14T04:51:16.0327147Z class GraphModule(torch.nn.Module):
2025-03-14T04:51:16.0327979Z     def forward(self, L_predictions_0_: "f32[3261, 81][81, 1]cpu", L_predictions_1_: "f32[3261, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"):
2025-03-14T04:51:16.0328785Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:51:16.0329016Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:51:16.0329337Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:51:16.0329744Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:51:16.0330145Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:51:16.0330540Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:51:16.0330960Z         
2025-03-14T04:51:16.0331682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:51:16.0332268Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:51:16.0332596Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:51:16.0332948Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:51:16.0333349Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:51:16.0333666Z         getitem_2: "Sym(1261 - s0)" = size_1[0]
2025-03-14T04:51:16.0334007Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:51:16.0334286Z         
2025-03-14T04:51:16.0334771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:51:16.0335766Z         proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:51:16.0336538Z         
2025-03-14T04:51:16.0337055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:51:16.0337682Z         deltas: "f32[3261, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:51:16.0338002Z         
2025-03-14T04:51:16.0338471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:51:16.0339049Z         boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:51:16.0339389Z         
2025-03-14T04:51:16.0339852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:51:16.0340404Z         getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:51:16.0340775Z         getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:51:16.0341152Z         widths: "f32[3261][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:51:16.0341541Z         
2025-03-14T04:51:16.0342023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:51:16.0342579Z         getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:51:16.0342974Z         getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:51:16.0343359Z         heights: "f32[3261][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:51:16.0343680Z         
2025-03-14T04:51:16.0344145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:51:16.0344686Z         getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:51:16.0344990Z         mul: "f32[3261][1]cpu" = 0.5 * widths
2025-03-14T04:51:16.0345335Z         ctr_x: "f32[3261][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:51:16.0345724Z         
2025-03-14T04:51:16.0346178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:51:16.0346750Z         getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:51:16.0347094Z         mul_1: "f32[3261][1]cpu" = 0.5 * heights
2025-03-14T04:51:16.0347434Z         ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:51:16.0347732Z         
2025-03-14T04:51:16.0348183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:51:16.0348775Z         getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:51:16.0359228Z         dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:51:16.0359610Z         
2025-03-14T04:51:16.0360079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:51:16.0360934Z         getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:51:16.0361290Z         dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:51:16.0361533Z         
2025-03-14T04:51:16.0361939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:51:16.0362460Z         getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:51:16.0362789Z         dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:51:16.0363024Z         
2025-03-14T04:51:16.0363424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:51:16.0363965Z         getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:51:16.0364314Z         dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:51:16.0364550Z         
2025-03-14T04:51:16.0364973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:51:16.0365513Z         dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:51:16.0365764Z         
2025-03-14T04:51:16.0366198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:51:16.0366721Z         dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:51:16.0366976Z         
2025-03-14T04:51:16.0367407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:51:16.0367953Z         getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:51:16.0368276Z         mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:51:16.0368613Z         getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:51:16.0368954Z         pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:51:16.0369209Z         
2025-03-14T04:51:16.0369646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:51:16.0370182Z         getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:51:16.0370509Z         mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:51:16.0370837Z         getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:51:16.0371182Z         pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:51:16.0371447Z         
2025-03-14T04:51:16.0371963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:51:16.0372507Z         exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:51:16.0372964Z         getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:51:16.0373333Z         pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:51:16.0373595Z         
2025-03-14T04:51:16.0374034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:51:16.0374620Z         exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:51:16.0374966Z         getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:51:16.0375329Z         pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:51:16.0375597Z         
2025-03-14T04:51:16.0376019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:51:16.0376506Z         mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:51:16.0376783Z         x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:51:16.0377030Z         
2025-03-14T04:51:16.0377439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:51:16.0377916Z         mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:51:16.0378187Z         y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:51:16.0378425Z         
2025-03-14T04:51:16.0378827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:51:16.0379317Z         mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:51:16.0379617Z         x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:51:16.0379875Z         
2025-03-14T04:51:16.0380279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:51:16.0380775Z         mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:51:16.0381064Z         y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:51:16.0381292Z         
2025-03-14T04:51:16.0381699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:51:16.0382251Z         pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:51:16.0382534Z         
2025-03-14T04:51:16.0382950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:51:16.0383495Z         predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320));  pred_boxes = None
2025-03-14T04:51:16.0383779Z         
2025-03-14T04:51:16.0384223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:51:16.0384823Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:51:16.0385183Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:51:16.0385469Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:51:16.0385769Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:51:16.0386087Z         getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:51:16.0386379Z         
2025-03-14T04:51:16.0386774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:51:16.0387353Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:51:16.0387690Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:51:16.0387926Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:51:16.0388280Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:51:16.0388616Z         getitem_26: "Sym(1261 - s0)" = size_3[0]
2025-03-14T04:51:16.0388853Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:51:16.0389063Z         
2025-03-14T04:51:16.0389472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:51:16.0390045Z         probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:51:16.0390361Z         
2025-03-14T04:51:16.0390786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:51:16.0391367Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:51:16.0391715Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:51:16.0391996Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:51:16.0392287Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:51:16.0392588Z         getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:51:16.0392841Z         
2025-03-14T04:51:16.0393376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:51:16.0394048Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:51:16.0394379Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:51:16.0394702Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:51:16.0395031Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:51:16.0395311Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:51:16.0395543Z         
2025-03-14T04:51:16.0395968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:51:16.0396476Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:51:16.0396706Z         
2025-03-14T04:51:18.2288452Z 
2025-03-14T04:51:18.2289391Z class GraphModule(torch.nn.Module):
2025-03-14T04:51:18.2289861Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T04:51:18.2290184Z         l_scores_0_ = L_scores_0_
2025-03-14T04:51:18.2290402Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:51:18.2290606Z         
2025-03-14T04:51:18.2291234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:51:18.2292647Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:51:18.2293059Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:51:18.2293440Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:51:18.2293917Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:51:18.2294228Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:51:18.2294485Z         
2025-03-14T04:51:18.2295008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:51:18.2295615Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:51:18.2295877Z         
2025-03-14T04:51:18.2299792Z 
2025-03-14T04:51:18.2299988Z class GraphModule(torch.nn.Module):
2025-03-14T04:51:18.2300374Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T04:51:18.2300700Z         l_scores_0_ = L_scores_0_
2025-03-14T04:51:18.2301027Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:51:18.2303759Z         
2025-03-14T04:51:18.2304533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:51:18.2305274Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:51:18.2305611Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:51:18.2305942Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:51:18.2306275Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:51:18.2306612Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:51:18.2306862Z         
2025-03-14T04:51:18.2307323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:51:18.2307864Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:51:18.2308107Z         
2025-03-14T04:51:48.8892197Z Compilation time (from dynamo_timed): 75.875589482
2025-03-14T04:51:48.8892541Z pass
2025-03-14T04:51:48.8896388Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:51:48.8897336Z TIMING: entire_frame_compile:75.87559 gc:0.04595 _recursive_pre_grad_passes:0.0334 _recursive_joint_graph_passes:0.26614 _recursive_post_grad_passes:0.27056 async_compile.wait:36.06714 code_gen:48.17276 inductor_compile:52.64282 backend_compile:61.26234 total_wall_time:75.87559
2025-03-14T04:51:48.8899072Z STATS: call_* op count: 777 | FakeTensorMode.__torch_dispatch__:33338 | FakeTensor.__torch_dispatch__:3861 | ProxyTorchDispatchMode.__torch_dispatch__:12075 | attempt fast:91 | slow no contiguity match:44 | fast is_contiguous:47
2025-03-14T04:51:48.8905224Z Dynamo produced 53 graphs covering 777 ops with 42 graph breaks (6 unique)
2025-03-14T04:51:54.4583481Z 
2025-03-14T04:52:05.9403769Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:52:05.9405660Z loading model: 0it [00:11, ?it/s]
2025-03-14T04:52:05.9419081Z cpu  eval  detectron2_fasterrcnn_r_101_dc5    
2025-03-14T04:52:24.0239741Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_101_dc5. Setting accuracy check to cosine
2025-03-14T04:52:24.0661614Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:52:38.9437843Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:52:53.1644602Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:53:04.4417950Z 
2025-03-14T04:53:04.4420353Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:04.4525712Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:53:04.4627531Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:53:04.4628091Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4629182Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4630014Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4630829Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4631572Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4632316Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4633125Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4633977Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4634835Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4635537Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4636196Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4636885Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4637630Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4638358Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4639099Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4639765Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4640511Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4641252Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4641974Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4642666Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4643331Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:04.4644048Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4644817Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4645568Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:04.4646334Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:04.4647051Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4647826Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4648622Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4649398Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4650134Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4650825Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4651622Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4652410Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4653171Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4653950Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4654665Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4655434Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4656265Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4657071Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4657871Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4658569Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4659291Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4660092Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4661007Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4661743Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4662439Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4663163Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4663937Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4664705Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4665437Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4666134Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4666853Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4667630Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4668455Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4669198Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4669887Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4670570Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4671303Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4672016Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4672699Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4673349Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4674028Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4674755Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4675476Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4676161Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4676813Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4677497Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4678232Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4678938Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4679626Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4680288Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:04.4680994Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4681757Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4682526Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:04.4683271Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:04.4683930Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4684606Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4685347Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4686103Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4686827Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4687526Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4688237Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4689015Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4689762Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4690531Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4691262Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4692090Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4692905Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4693673Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4694481Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4695180Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4695936Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4696772Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4697553Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4698361Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4699071Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4699823Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4700597Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4701306Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4701995Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4702649Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4703326Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4704061Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4704763Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4705452Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4706108Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4706788Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4707520Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4708228Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4708914Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4709564Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4710271Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4711012Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4711761Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4712449Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4713097Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4713782Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4714515Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4715231Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4715923Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4716611Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4717339Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4718083Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4718804Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4719491Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4720143Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4720827Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4721567Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4722286Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4722974Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4723630Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4724341Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4725079Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4725823Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4726535Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4727223Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:04.4727932Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4728711Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4729496Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:04.4730256Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:04.4730965Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4731763Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4732555Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4733322Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4734096Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4734803Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4735543Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4736332Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4737097Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4737826Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4738553Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4739301Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4740117Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4740887Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4741606Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4742262Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4742943Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4743677Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4744388Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4745076Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4745729Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4746408Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4747141Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4747849Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4748545Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4749250Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4749959Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4750701Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4751414Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4752143Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4752836Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4753561Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4754350Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4755054Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4755733Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4756386Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4757067Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4757815Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4758545Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4759268Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4759960Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4760823Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4761609Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4762357Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4763094Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4763783Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4764501Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4765279Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4766026Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4766850Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4767548Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4768315Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4769088Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4769840Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4770558Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4771241Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4772053Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4772892Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4773703Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4774433Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4775128Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4775815Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4776549Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4777257Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4777951Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4778605Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4779285Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4780019Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4780734Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4781461Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4782142Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4782818Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4783550Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4784259Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4784941Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4785598Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4786275Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4787004Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4787715Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4788405Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4789063Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4789775Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4790505Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4791215Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4791899Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4792549Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4793222Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4793948Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4794688Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4795378Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4796063Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4796752Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4797488Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4798201Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4798888Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4799539Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4800221Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4800947Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4801661Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4802347Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4802999Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4803676Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4804403Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4805117Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4805815Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4806522Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4807237Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4808082Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4808882Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4809683Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4810424Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4811180Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4812063Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4812891Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4813658Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4814396Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4815134Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4815909Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4816686Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4817427Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4818111Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4818820Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4819606Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4820350Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4821069Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4821747Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4822461Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4823231Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4823943Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4824686Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4825333Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4826012Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4826754Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4827464Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4828147Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4828806Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4829495Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4830232Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4830944Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4831633Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4832288Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4832965Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4833695Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4834410Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4835099Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4835753Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4836495Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4837300Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4838099Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4838802Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4839456Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4840138Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4840875Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4841593Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4842276Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4842928Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4843615Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4844367Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4845082Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4845778Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4846436Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4847121Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4847896Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4848644Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4849329Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4849988Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4850692Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4851527Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4852335Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4853056Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4853710Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4854396Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4855135Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4855842Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4856553Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4857248Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4857934Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4858675Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4859393Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4860075Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4860845Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4861595Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4862365Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4863094Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4863831Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4864600Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4865348Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4866221Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4866978Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4867705Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4868395Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4869110Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4869894Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4870646Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4871372Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4872035Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4872717Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4873451Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4874164Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4874866Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4875547Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4876263Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4877049Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4877817Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4879364Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4880098Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4880852Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4881614Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4882565Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4883413Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4884072Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4884776Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4885526Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4886265Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4886978Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4887656Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4888364Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4889125Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4889889Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4890624Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4891330Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4892123Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4892907Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4893713Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4894442Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4895173Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4895922Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4896679Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4897411Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4898121Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4898799Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4899502Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4900261Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4900976Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4901678Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4902347Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4903024Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4903770Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4904501Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4905208Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4905879Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4906584Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4907335Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4908100Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4908841Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4909522Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4910249Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4911010Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4911751Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4912478Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4913141Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4913837Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4914579Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4915303Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4916002Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4916668Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4917377Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4918158Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4918883Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4919604Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4920289Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4921004Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4921798Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4922536Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4923285Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4923968Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4924690Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4925473Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4926241Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4926973Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4927672Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4928397Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4929180Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4929943Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4930674Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4931371Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4932161Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4932988Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4933777Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4934501Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4935196Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4935933Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4936699Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4937460Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4938173Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4938845Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4939546Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4940302Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4941033Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4941742Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4942410Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4943111Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4943865Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4944608Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4945300Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4945971Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4946670Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4947428Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4948168Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4948879Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4949587Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4950313Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4951127Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4951874Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4952587Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4953276Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4953980Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4954750Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4955499Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4956216Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4956895Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4957596Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4958361Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4959094Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4959810Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4960622Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4961374Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4962179Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4962955Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4963697Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4964463Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4965208Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4966053Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4966790Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4967514Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4968212Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4968931Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4969718Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4970474Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4971203Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4971967Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4972738Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4973505Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4974242Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4974928Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4975580Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4976262Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4977001Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4977707Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4978423Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4979079Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4979800Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4980534Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4981244Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4981930Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4982595Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:04.4983304Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4984058Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4984801Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:04.4985518Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:04.4986182Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4986858Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4987585Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4988293Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4988981Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.4989628Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:04.4990302Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4991029Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4991769Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.4992464Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.4993156Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:04.4993844Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4994574Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4995281Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.4995959Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.4996609Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:04.4997285Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.4998012Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.4998722Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.4999422Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5000092Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5000780Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5001530Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5002255Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5002953Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5003623Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5004314Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5005059Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5005812Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5006533Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5007302Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:53:04.5008046Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:53:04.5008744Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:53:04.5009538Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:53:04.5010357Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:53:04.5011158Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:53:04.5012000Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:53:04.5012513Z         
2025-03-14T04:53:04.5012952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5013859Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5014530Z         
2025-03-14T04:53:04.5014921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5016832Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5018565Z         
2025-03-14T04:53:04.5018971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:53:04.5019484Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:53:04.5019760Z         
2025-03-14T04:53:04.5020240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:53:04.5020972Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:53:04.5021344Z         
2025-03-14T04:53:04.5021733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5022515Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5023079Z         
2025-03-14T04:53:04.5023450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5025417Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5027135Z         
2025-03-14T04:53:04.5027510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5027991Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:53:04.5028247Z         
2025-03-14T04:53:04.5028580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5029369Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5029904Z         
2025-03-14T04:53:04.5030253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5032107Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5033737Z         
2025-03-14T04:53:04.5034107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5034590Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:53:04.5034849Z         
2025-03-14T04:53:04.5035225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5035972Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5036554Z         
2025-03-14T04:53:04.5036905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5038765Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5040407Z         
2025-03-14T04:53:04.5040752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5041501Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:04.5042057Z         
2025-03-14T04:53:04.5042403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5044326Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5046040Z         
2025-03-14T04:53:04.5046407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5046883Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:53:04.5047141Z         
2025-03-14T04:53:04.5047508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5048012Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:53:04.5048295Z         
2025-03-14T04:53:04.5048671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5049453Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5050077Z         
2025-03-14T04:53:04.5050446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5052608Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5054339Z         
2025-03-14T04:53:04.5054708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5055190Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:53:04.5055452Z         
2025-03-14T04:53:04.5055788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5056527Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5057076Z         
2025-03-14T04:53:04.5057422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5059346Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5061200Z         
2025-03-14T04:53:04.5061579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5062081Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:53:04.5062351Z         
2025-03-14T04:53:04.5062705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5063544Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5064172Z         
2025-03-14T04:53:04.5064542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5066517Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5068250Z         
2025-03-14T04:53:04.5068633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5069148Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:53:04.5069428Z         
2025-03-14T04:53:04.5069811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5070324Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:53:04.5070603Z         
2025-03-14T04:53:04.5070947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5071683Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5072228Z         
2025-03-14T04:53:04.5072572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5074433Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5076074Z         
2025-03-14T04:53:04.5076441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5076919Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:53:04.5077175Z         
2025-03-14T04:53:04.5077537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5078270Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5078838Z         
2025-03-14T04:53:04.5079185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5081033Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5082680Z         
2025-03-14T04:53:04.5083046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5083520Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:53:04.5083774Z         
2025-03-14T04:53:04.5084104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5084837Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5085386Z         
2025-03-14T04:53:04.5085740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5087703Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5089431Z         
2025-03-14T04:53:04.5089809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5090347Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:53:04.5090644Z         
2025-03-14T04:53:04.5091052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5091711Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:53:04.5092027Z         
2025-03-14T04:53:04.5092411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5093227Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5093799Z         
2025-03-14T04:53:04.5094165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5096124Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5097861Z         
2025-03-14T04:53:04.5098244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5098750Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:53:04.5099027Z         
2025-03-14T04:53:04.5099373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5100145Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5100714Z         
2025-03-14T04:53:04.5101076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5103252Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5104917Z         
2025-03-14T04:53:04.5105303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5105798Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:53:04.5106073Z         
2025-03-14T04:53:04.5106475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5107259Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5107871Z         
2025-03-14T04:53:04.5108237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5110203Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5111934Z         
2025-03-14T04:53:04.5112285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5113078Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:04.5113669Z         
2025-03-14T04:53:04.5114034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5116047Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5117831Z         
2025-03-14T04:53:04.5118210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5118721Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:53:04.5119000Z         
2025-03-14T04:53:04.5119385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5119904Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:53:04.5120189Z         
2025-03-14T04:53:04.5120540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5121348Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5121992Z         
2025-03-14T04:53:04.5122380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5124364Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5126215Z         
2025-03-14T04:53:04.5126622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5127159Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:53:04.5127453Z         
2025-03-14T04:53:04.5127822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5128662Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5129292Z         
2025-03-14T04:53:04.5129671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5131831Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5133774Z         
2025-03-14T04:53:04.5134167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5134708Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:53:04.5135004Z         
2025-03-14T04:53:04.5135385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5136258Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5136882Z         
2025-03-14T04:53:04.5137307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5139389Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5141240Z         
2025-03-14T04:53:04.5141639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5142178Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:53:04.5142479Z         
2025-03-14T04:53:04.5142885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5143424Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:53:04.5143730Z         
2025-03-14T04:53:04.5144104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5144923Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5145494Z         
2025-03-14T04:53:04.5145855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5147810Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5149543Z         
2025-03-14T04:53:04.5149927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5150427Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:53:04.5150701Z         
2025-03-14T04:53:04.5151091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5151859Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5152473Z         
2025-03-14T04:53:04.5152837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5154782Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5156517Z         
2025-03-14T04:53:04.5156904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5157412Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:53:04.5157691Z         
2025-03-14T04:53:04.5158040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5158820Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5159402Z         
2025-03-14T04:53:04.5159765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5161929Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5163684Z         
2025-03-14T04:53:04.5164071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5164589Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:53:04.5164875Z         
2025-03-14T04:53:04.5165274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5165881Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:53:04.5166191Z         
2025-03-14T04:53:04.5166587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5167451Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5168055Z         
2025-03-14T04:53:04.5168439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5170555Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5172482Z         
2025-03-14T04:53:04.5172909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5173442Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:53:04.5173733Z         
2025-03-14T04:53:04.5174107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5174924Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5175531Z         
2025-03-14T04:53:04.5175919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5177996Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5179712Z         
2025-03-14T04:53:04.5180076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5180548Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:53:04.5180807Z         
2025-03-14T04:53:04.5181171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5181913Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5182492Z         
2025-03-14T04:53:04.5182842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5184679Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5186306Z         
2025-03-14T04:53:04.5186664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5187150Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:53:04.5187423Z         
2025-03-14T04:53:04.5187787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5188265Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:53:04.5188533Z         
2025-03-14T04:53:04.5188868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5189590Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5190122Z         
2025-03-14T04:53:04.5190466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5192337Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5194094Z         
2025-03-14T04:53:04.5194483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5195023Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:53:04.5195293Z         
2025-03-14T04:53:04.5195644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5196453Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5197022Z         
2025-03-14T04:53:04.5197388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5199350Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5201079Z         
2025-03-14T04:53:04.5201464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5201966Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:53:04.5202238Z         
2025-03-14T04:53:04.5202586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5203365Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5203904Z         
2025-03-14T04:53:04.5204268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5206214Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5207929Z         
2025-03-14T04:53:04.5208277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5209088Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:04.5209673Z         
2025-03-14T04:53:04.5210036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5212233Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5214101Z         
2025-03-14T04:53:04.5214485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5214976Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:53:04.5215247Z         
2025-03-14T04:53:04.5215652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5216185Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:53:04.5216477Z         
2025-03-14T04:53:04.5216847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5217603Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5218169Z         
2025-03-14T04:53:04.5218529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5220475Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5222194Z         
2025-03-14T04:53:04.5222584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5223087Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:53:04.5223352Z         
2025-03-14T04:53:04.5223701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5224512Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5225119Z         
2025-03-14T04:53:04.5225485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5227433Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5229169Z         
2025-03-14T04:53:04.5229559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5230057Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:53:04.5230326Z         
2025-03-14T04:53:04.5230674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5231441Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5232007Z         
2025-03-14T04:53:04.5232364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5234247Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5235883Z         
2025-03-14T04:53:04.5236244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5236724Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:53:04.5236988Z         
2025-03-14T04:53:04.5237360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5237838Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:53:04.5238100Z         
2025-03-14T04:53:04.5238469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5239191Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5239758Z         
2025-03-14T04:53:04.5240104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5241944Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5243630Z         
2025-03-14T04:53:04.5244010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5244503Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:53:04.5244767Z         
2025-03-14T04:53:04.5245107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5245877Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5246482Z         
2025-03-14T04:53:04.5246870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5248959Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5250841Z         
2025-03-14T04:53:04.5251255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5251854Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:53:04.5252146Z         
2025-03-14T04:53:04.5252534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5253416Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5254032Z         
2025-03-14T04:53:04.5254398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5256351Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5258077Z         
2025-03-14T04:53:04.5258459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5258961Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:53:04.5259240Z         
2025-03-14T04:53:04.5259633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5260146Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:53:04.5260420Z         
2025-03-14T04:53:04.5260908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5261676Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5262248Z         
2025-03-14T04:53:04.5262625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5264523Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5266199Z         
2025-03-14T04:53:04.5266569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5267040Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:53:04.5267298Z         
2025-03-14T04:53:04.5267708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5268479Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5269099Z         
2025-03-14T04:53:04.5269472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5271339Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5273007Z         
2025-03-14T04:53:04.5273391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5273890Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:53:04.5274159Z         
2025-03-14T04:53:04.5274515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5275298Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5275842Z         
2025-03-14T04:53:04.5276187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5278135Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5279981Z         
2025-03-14T04:53:04.5280387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5280928Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:53:04.5281207Z         
2025-03-14T04:53:04.5281593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5282156Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:53:04.5282432Z         
2025-03-14T04:53:04.5282777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5283572Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5284132Z         
2025-03-14T04:53:04.5284497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5286505Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5288346Z         
2025-03-14T04:53:04.5288753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5289297Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:53:04.5289587Z         
2025-03-14T04:53:04.5289967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5290811Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5291480Z         
2025-03-14T04:53:04.5291889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5294035Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5295858Z         
2025-03-14T04:53:04.5296265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5296790Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:53:04.5297070Z         
2025-03-14T04:53:04.5297465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5298290Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5298930Z         
2025-03-14T04:53:04.5299322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5301440Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5303263Z         
2025-03-14T04:53:04.5303676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5304228Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:53:04.5304531Z         
2025-03-14T04:53:04.5304954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5305504Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:53:04.5305813Z         
2025-03-14T04:53:04.5306184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5306992Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5307583Z         
2025-03-14T04:53:04.5307967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5310103Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5311974Z         
2025-03-14T04:53:04.5312387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5312980Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:53:04.5313272Z         
2025-03-14T04:53:04.5313647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5314518Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5315139Z         
2025-03-14T04:53:04.5315541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5317667Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5319548Z         
2025-03-14T04:53:04.5319971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5320491Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:53:04.5320760Z         
2025-03-14T04:53:04.5321106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5321876Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5322446Z         
2025-03-14T04:53:04.5322809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5324821Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5326653Z         
2025-03-14T04:53:04.5327051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5327583Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:53:04.5327878Z         
2025-03-14T04:53:04.5328317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5328853Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:53:04.5329180Z         
2025-03-14T04:53:04.5329561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5330385Z         x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5330992Z         
2025-03-14T04:53:04.5331387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5333665Z         x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5335522Z         
2025-03-14T04:53:04.5335941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5336467Z         out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:53:04.5336765Z         
2025-03-14T04:53:04.5337137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5337951Z         x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5338548Z         
2025-03-14T04:53:04.5338929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5340998Z         x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5342818Z         
2025-03-14T04:53:04.5343222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5343780Z         out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:53:04.5344063Z         
2025-03-14T04:53:04.5344432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5345285Z         x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5345885Z         
2025-03-14T04:53:04.5346272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5348364Z         x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5350198Z         
2025-03-14T04:53:04.5350602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5351140Z         x_93 += out_51;  out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:53:04.5351419Z         
2025-03-14T04:53:04.5351806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5352315Z         out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:53:04.5352592Z         
2025-03-14T04:53:04.5352936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5353705Z         x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5354264Z         
2025-03-14T04:53:04.5354628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5356570Z         x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5358288Z         
2025-03-14T04:53:04.5358707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5359223Z         out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:53:04.5359543Z         
2025-03-14T04:53:04.5359896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5360760Z         x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5361349Z         
2025-03-14T04:53:04.5361720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5363656Z         x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5365385Z         
2025-03-14T04:53:04.5365765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5366268Z         out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:53:04.5366529Z         
2025-03-14T04:53:04.5366885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5367697Z         x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5368306Z         
2025-03-14T04:53:04.5368684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5370763Z         x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5372642Z         
2025-03-14T04:53:04.5373045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5373642Z         x_99 += out_55;  out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:53:04.5373935Z         
2025-03-14T04:53:04.5374334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5374917Z         out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:53:04.5375208Z         
2025-03-14T04:53:04.5375576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5376386Z         x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5376983Z         
2025-03-14T04:53:04.5377368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5379435Z         x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5381170Z         
2025-03-14T04:53:04.5381531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5382009Z         out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:53:04.5382267Z         
2025-03-14T04:53:04.5382593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5383320Z         x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5383891Z         
2025-03-14T04:53:04.5384252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5386227Z         x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5386296Z         
2025-03-14T04:53:04.5386636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5386782Z         out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:53:04.5386887Z         
2025-03-14T04:53:04.5387148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5387598Z         x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5387662Z         
2025-03-14T04:53:04.5387944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5389568Z         x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5389640Z         
2025-03-14T04:53:04.5389956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5390132Z         x_105 += out_59;  out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:53:04.5390207Z         
2025-03-14T04:53:04.5390501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5390653Z         out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:53:04.5390717Z         
2025-03-14T04:53:04.5390980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5391419Z         x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5391487Z         
2025-03-14T04:53:04.5391744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5393320Z         x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5393390Z         
2025-03-14T04:53:04.5393676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5393849Z         out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:53:04.5393909Z         
2025-03-14T04:53:04.5394159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5394579Z         x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5394658Z         
2025-03-14T04:53:04.5394938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5396571Z         x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5396645Z         
2025-03-14T04:53:04.5396952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5397094Z         out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:53:04.5397155Z         
2025-03-14T04:53:04.5397409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5397853Z         x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5397925Z         
2025-03-14T04:53:04.5398202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5399826Z         x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5399901Z         
2025-03-14T04:53:04.5400221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5400389Z         x_111 += out_63;  out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:53:04.5400490Z         
2025-03-14T04:53:04.5400796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5400940Z         out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:53:04.5401015Z         
2025-03-14T04:53:04.5401273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5401726Z         x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5401801Z         
2025-03-14T04:53:04.5402081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5403710Z         x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5403777Z         
2025-03-14T04:53:04.5404082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5404220Z         out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:53:04.5404293Z         
2025-03-14T04:53:04.5404549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5405002Z         x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5405074Z         
2025-03-14T04:53:04.5405350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5407117Z         x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5407186Z         
2025-03-14T04:53:04.5407544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5407690Z         out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:53:04.5407766Z         
2025-03-14T04:53:04.5408041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5408533Z         x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5408610Z         
2025-03-14T04:53:04.5408917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5410738Z         x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5410809Z         
2025-03-14T04:53:04.5411133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5411316Z         x_117 += out_67;  out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:53:04.5411387Z         
2025-03-14T04:53:04.5411772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5411934Z         out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:53:04.5412011Z         
2025-03-14T04:53:04.5412306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5412781Z         x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5412852Z         
2025-03-14T04:53:04.5413158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5415644Z         x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5415773Z         
2025-03-14T04:53:04.5416091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5416233Z         out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:53:04.5416309Z         
2025-03-14T04:53:04.5416579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5417058Z         x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5417131Z         
2025-03-14T04:53:04.5417424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5419114Z         x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5419193Z         
2025-03-14T04:53:04.5419513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5419659Z         out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:53:04.5419735Z         
2025-03-14T04:53:04.5420007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5420495Z         x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5420563Z         
2025-03-14T04:53:04.5420863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5422613Z         x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5422723Z         
2025-03-14T04:53:04.5423049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5423207Z         x_123 += out_71;  out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:53:04.5423280Z         
2025-03-14T04:53:04.5423574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5423726Z         out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:53:04.5423790Z         
2025-03-14T04:53:04.5424062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5424500Z         x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5424573Z         
2025-03-14T04:53:04.5424850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5426484Z         x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5426559Z         
2025-03-14T04:53:04.5426857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5427003Z         out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:53:04.5427067Z         
2025-03-14T04:53:04.5427337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5427780Z         x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5427855Z         
2025-03-14T04:53:04.5428131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5429787Z         x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5429893Z         
2025-03-14T04:53:04.5430188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5430333Z         out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:53:04.5430398Z         
2025-03-14T04:53:04.5430666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5431112Z         x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5431187Z         
2025-03-14T04:53:04.5431460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5433084Z         x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5433162Z         
2025-03-14T04:53:04.5433452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5433615Z         x_129 += out_75;  out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:53:04.5433679Z         
2025-03-14T04:53:04.5433979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5434124Z         out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:53:04.5434197Z         
2025-03-14T04:53:04.5434456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5434903Z         x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5434968Z         
2025-03-14T04:53:04.5435250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5436862Z         x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5436956Z         
2025-03-14T04:53:04.5437243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5437373Z         out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:53:04.5437443Z         
2025-03-14T04:53:04.5437691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5438120Z         x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5438183Z         
2025-03-14T04:53:04.5438452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5439995Z         x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5440058Z         
2025-03-14T04:53:04.5440342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5440472Z         out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:53:04.5440540Z         
2025-03-14T04:53:04.5440791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5441243Z         x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5441310Z         
2025-03-14T04:53:04.5441594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5443248Z         x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5443339Z         
2025-03-14T04:53:04.5443623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5443773Z         x_135 += out_79;  out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:53:04.5443842Z         
2025-03-14T04:53:04.5444126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5444279Z         out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:53:04.5444342Z         
2025-03-14T04:53:04.5444617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5445063Z         x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5445130Z         
2025-03-14T04:53:04.5445412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5447044Z         x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5447122Z         
2025-03-14T04:53:04.5447418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5447568Z         out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:53:04.5447632Z         
2025-03-14T04:53:04.5447912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5448394Z         x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5448464Z         
2025-03-14T04:53:04.5448766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5450605Z         x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5450714Z         
2025-03-14T04:53:04.5451037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5451198Z         out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:53:04.5451276Z         
2025-03-14T04:53:04.5451623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5452126Z         x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5452198Z         
2025-03-14T04:53:04.5452510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5454293Z         x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5454377Z         
2025-03-14T04:53:04.5454705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5454874Z         x_141 += out_83;  out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:53:04.5454957Z         
2025-03-14T04:53:04.5455283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5455453Z         out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:53:04.5455524Z         
2025-03-14T04:53:04.5455817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5456293Z         x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5456368Z         
2025-03-14T04:53:04.5456712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5458599Z         x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5458708Z         
2025-03-14T04:53:04.5459044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5459209Z         out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:53:04.5459286Z         
2025-03-14T04:53:04.5459581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5460075Z         x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5460170Z         
2025-03-14T04:53:04.5460466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5462276Z         x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5462358Z         
2025-03-14T04:53:04.5462659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5462808Z         out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:53:04.5462873Z         
2025-03-14T04:53:04.5463143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5463592Z         x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5463666Z         
2025-03-14T04:53:04.5463940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5465641Z         x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5465758Z         
2025-03-14T04:53:04.5466051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5466215Z         x_147 += out_87;  out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:53:04.5466280Z         
2025-03-14T04:53:04.5466583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5466731Z         out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:53:04.5466805Z         
2025-03-14T04:53:04.5467067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5467521Z         x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5467586Z         
2025-03-14T04:53:04.5467877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5469516Z         x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5469584Z         
2025-03-14T04:53:04.5469891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5470032Z         out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:53:04.5470104Z         
2025-03-14T04:53:04.5470364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5470820Z         x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5470883Z         
2025-03-14T04:53:04.5471198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5472792Z         x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5472887Z         
2025-03-14T04:53:04.5473175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5473306Z         out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:53:04.5473377Z         
2025-03-14T04:53:04.5473623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5474060Z         x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5474121Z         
2025-03-14T04:53:04.5474394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5475945Z         x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5476010Z         
2025-03-14T04:53:04.5476294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5476439Z         x_153 += out_91;  out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:53:04.5476512Z         
2025-03-14T04:53:04.5476790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5476937Z         out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:53:04.5477001Z         
2025-03-14T04:53:04.5477255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5477700Z         x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5477772Z         
2025-03-14T04:53:04.5478035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5479627Z         x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5479700Z         
2025-03-14T04:53:04.5479985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5480130Z         out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:53:04.5480193Z         
2025-03-14T04:53:04.5480460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5480896Z         x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5480969Z         
2025-03-14T04:53:04.5481228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5482766Z         x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5482837Z         
2025-03-14T04:53:04.5483115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5483255Z         out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:53:04.5483315Z         
2025-03-14T04:53:04.5483566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5483990Z         x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5484056Z         
2025-03-14T04:53:04.5484351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5485954Z         x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5486058Z         
2025-03-14T04:53:04.5486351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5486516Z         x_159 += out_95;  out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:53:04.5486580Z         
2025-03-14T04:53:04.5486883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5487026Z         out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:53:04.5487097Z         
2025-03-14T04:53:04.5487360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5487817Z         x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5487892Z         
2025-03-14T04:53:04.5488170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5489811Z         x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5489877Z         
2025-03-14T04:53:04.5490182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5490336Z         out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:53:04.5490400Z         
2025-03-14T04:53:04.5490671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5491156Z         x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5491227Z         
2025-03-14T04:53:04.5491572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5493429Z         x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5493507Z         
2025-03-14T04:53:04.5493812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5493962Z         out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:53:04.5494025Z         
2025-03-14T04:53:04.5494294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5494754Z         x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5494824Z         
2025-03-14T04:53:04.5495100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5496763Z         x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5496837Z         
2025-03-14T04:53:04.5497133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5497301Z         x_165 += out_99;  out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:53:04.5497366Z         
2025-03-14T04:53:04.5497671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5497822Z         out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:53:04.5497890Z         
2025-03-14T04:53:04.5498186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5498646Z         x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5498735Z         
2025-03-14T04:53:04.5499004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5500546Z         x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5500618Z         
2025-03-14T04:53:04.5500905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5501038Z         out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:53:04.5501105Z         
2025-03-14T04:53:04.5501349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5501784Z         x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5501848Z         
2025-03-14T04:53:04.5502117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5503648Z         x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5503725Z         
2025-03-14T04:53:04.5504013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5504144Z         out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:53:04.5504211Z         
2025-03-14T04:53:04.5504457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5504927Z         x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5505023Z         
2025-03-14T04:53:04.5505292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5506838Z         x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5506904Z         
2025-03-14T04:53:04.5507187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5507344Z         x_171 += out_103;  out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:53:04.5507412Z         
2025-03-14T04:53:04.5507689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5507839Z         out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:53:04.5507901Z         
2025-03-14T04:53:04.5508152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5508575Z         x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5508645Z         
2025-03-14T04:53:04.5508905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5510461Z         x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5510532Z         
2025-03-14T04:53:04.5510813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5510951Z         out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:53:04.5511011Z         
2025-03-14T04:53:04.5511303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5511729Z         x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5511826Z         
2025-03-14T04:53:04.5512089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5513641Z         x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5513715Z         
2025-03-14T04:53:04.5513993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5514136Z         out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:53:04.5514197Z         
2025-03-14T04:53:04.5514453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5514880Z         x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5514951Z         
2025-03-14T04:53:04.5515212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5516750Z         x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5516823Z         
2025-03-14T04:53:04.5517097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5517262Z         x_177 += out_107;  out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:53:04.5517323Z         
2025-03-14T04:53:04.5517640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5517783Z         out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:53:04.5517851Z         
2025-03-14T04:53:04.5518095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5518553Z         x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5518623Z         
2025-03-14T04:53:04.5518885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5520427Z         x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5520492Z         
2025-03-14T04:53:04.5520782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5520915Z         out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:53:04.5520981Z         
2025-03-14T04:53:04.5521226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5521660Z         x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5521726Z         
2025-03-14T04:53:04.5521983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5523513Z         x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5523575Z         
2025-03-14T04:53:04.5523860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5523990Z         out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:53:04.5524088Z         
2025-03-14T04:53:04.5524345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5524812Z         x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5524879Z         
2025-03-14T04:53:04.5525142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5526686Z         x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5526749Z         
2025-03-14T04:53:04.5527030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5527191Z         x_183 += out_111;  out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:53:04.5527251Z         
2025-03-14T04:53:04.5527540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5527680Z         out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:53:04.5527750Z         
2025-03-14T04:53:04.5528001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5528433Z         x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5528495Z         
2025-03-14T04:53:04.5528776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5530368Z         x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5530441Z         
2025-03-14T04:53:04.5530803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5530946Z         out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:53:04.5531049Z         
2025-03-14T04:53:04.5531316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5531830Z         x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5531903Z         
2025-03-14T04:53:04.5532188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5533943Z         x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5534013Z         
2025-03-14T04:53:04.5534295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5534423Z         out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:53:04.5534490Z         
2025-03-14T04:53:04.5534727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5535166Z         x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5535230Z         
2025-03-14T04:53:04.5535496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5537048Z         x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5537122Z         
2025-03-14T04:53:04.5537402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5537587Z         x_189 += out_115;  out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:53:04.5537659Z         
2025-03-14T04:53:04.5537942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5538121Z         out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:53:04.5538181Z         
2025-03-14T04:53:04.5538432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5538848Z         x_190: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5538916Z         
2025-03-14T04:53:04.5539178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5540765Z         x_191: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_190 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5540837Z         
2025-03-14T04:53:04.5541119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5541261Z         out_120: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_191);  x_191 = None
2025-03-14T04:53:04.5541321Z         
2025-03-14T04:53:04.5541573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5541999Z         x_192: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_120 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5542067Z         
2025-03-14T04:53:04.5542330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5543881Z         x_193: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_192 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5543953Z         
2025-03-14T04:53:04.5544264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5544405Z         out_121: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_193);  x_193 = None
2025-03-14T04:53:04.5544498Z         
2025-03-14T04:53:04.5544762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5545230Z         x_194: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_121 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5545305Z         
2025-03-14T04:53:04.5545596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5547269Z         x_195: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_194 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5547344Z         
2025-03-14T04:53:04.5547606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5548077Z         x_196: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_119 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:04.5548147Z         
2025-03-14T04:53:04.5548427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5550124Z         x_197: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_196 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5550193Z         
2025-03-14T04:53:04.5550486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5550640Z         x_195 += x_197;  out_122: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_195;  x_195 = x_197 = None
2025-03-14T04:53:04.5550712Z         
2025-03-14T04:53:04.5551037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5551196Z         out_123: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_122);  out_122 = None
2025-03-14T04:53:04.5551262Z         
2025-03-14T04:53:04.5551579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5552010Z         x_198: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5552077Z         
2025-03-14T04:53:04.5552337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5553897Z         x_199: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_198 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5553973Z         
2025-03-14T04:53:04.5554270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5554418Z         out_124: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_199);  x_199 = None
2025-03-14T04:53:04.5554481Z         
2025-03-14T04:53:04.5554750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5555201Z         x_200: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_124 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5555271Z         
2025-03-14T04:53:04.5555546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5557188Z         x_201: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_200 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5557258Z         
2025-03-14T04:53:04.5557540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5557678Z         out_125: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_201);  x_201 = None
2025-03-14T04:53:04.5557775Z         
2025-03-14T04:53:04.5558035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5558493Z         x_202: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_125 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5558560Z         
2025-03-14T04:53:04.5558824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5560445Z         x_203: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_202 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5560606Z         
2025-03-14T04:53:04.5560909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5561084Z         x_203 += out_123;  out_126: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_203;  x_203 = out_123 = None
2025-03-14T04:53:04.5561151Z         
2025-03-14T04:53:04.5561455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5561606Z         out_127: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_126);  out_126 = None
2025-03-14T04:53:04.5561684Z         
2025-03-14T04:53:04.5561944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5562395Z         x_204: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5562473Z         
2025-03-14T04:53:04.5562747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5564377Z         x_205: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_204 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5564445Z         
2025-03-14T04:53:04.5564804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5564946Z         out_128: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_205);  x_205 = None
2025-03-14T04:53:04.5565058Z         
2025-03-14T04:53:04.5565317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5565774Z         x_206: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_128 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5565847Z         
2025-03-14T04:53:04.5566126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5567734Z         x_207: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_206 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5567801Z         
2025-03-14T04:53:04.5568110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5568259Z         out_129: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_207);  x_207 = None
2025-03-14T04:53:04.5568323Z         
2025-03-14T04:53:04.5568587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5569042Z         x_208: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_129 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5569113Z         
2025-03-14T04:53:04.5569401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5571115Z         x_209: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_208 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5571191Z         
2025-03-14T04:53:04.5571548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5571779Z         x_209 += out_127;  out_130: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_209;  x_209 = out_127 = None
2025-03-14T04:53:04.5571849Z         
2025-03-14T04:53:04.5572176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5572381Z         out_131: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_130);  out_130 = None
2025-03-14T04:53:04.5572455Z         
2025-03-14T04:53:04.5572953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:04.5573120Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:53:04.5573184Z         
2025-03-14T04:53:04.5573503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:04.5573647Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:53:04.5573720Z         
2025-03-14T04:53:04.5574182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:04.5574349Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:53:04.5574413Z         
2025-03-14T04:53:04.5574729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:04.5574873Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:53:04.5574942Z         
2025-03-14T04:53:04.5575338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:53:04.5575532Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:53:04.5575636Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:53:04.5575767Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:53:04.5575830Z         
2025-03-14T04:53:04.5576181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:53:04.5576310Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:53:04.5576381Z         
2025-03-14T04:53:04.5576722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:53:04.5576852Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:53:04.5576913Z         
2025-03-14T04:53:04.5577297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:53:04.5577511Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:53:04.5577579Z         
2025-03-14T04:53:04.5577999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:53:04.5578127Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:53:04.5578585Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:53:04.5578801Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:53:04.5578923Z         x_210: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:53:04.5578986Z         
2025-03-14T04:53:04.5579293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:04.5579416Z         tensor: "f32[82125, 4][4, 1]cpu" = x_210.to(torch.float32);  x_210 = None
2025-03-14T04:53:04.5579486Z         
2025-03-14T04:53:04.5579737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5580515Z         x_211: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_131, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_131 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:53:04.5580582Z         
2025-03-14T04:53:04.5580861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:53:04.5581051Z         x_212: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_211, inplace = False);  x_211 = None
2025-03-14T04:53:04.5581121Z         
2025-03-14T04:53:04.5581505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:53:04.5582363Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:53:04.5582436Z         
2025-03-14T04:53:04.5582793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:53:04.5583626Z         x_213: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_212 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:53:04.5583690Z         
2025-03-14T04:53:04.5584032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:53:04.5584183Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:53:04.5584326Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:53:04.5584387Z         
2025-03-14T04:53:04.5584841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:53:04.5585000Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_213.view(4, -1, 4, 73, 75);  x_213 = None
2025-03-14T04:53:04.5585210Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:53:04.5585384Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:53:04.5585456Z         
2025-03-14T04:53:04.5585853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:53:04.5586063Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:53:04.5586131Z         
2025-03-14T04:53:04.5586565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:53:04.5586720Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:53:04.5586864Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:53:04.5587006Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:53:04.5587065Z         
2025-03-14T04:53:04.5587447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:04.5587616Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:53:04.5587686Z         
2025-03-14T04:53:04.5587999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:04.5588144Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:53:04.5588208Z         
2025-03-14T04:53:04.5588523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:04.5588654Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:04.5588793Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:04.5588943Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:53:04.5589013Z         
2025-03-14T04:53:04.5589347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:04.5589477Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:04.5589599Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:04.5589762Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:04.5589825Z         
2025-03-14T04:53:04.5590154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:04.5590272Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:04.5590365Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:53:04.5590484Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:53:04.5590552Z         
2025-03-14T04:53:04.5590892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:04.5591043Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:04.5591174Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:53:04.5591305Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:53:04.5591365Z         
2025-03-14T04:53:04.5591724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:04.5594546Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:04.5594672Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:53:04.5594737Z         
2025-03-14T04:53:04.5595063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:04.5595212Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:04.5595334Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:53:04.5595397Z         
2025-03-14T04:53:04.5595705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:04.5595854Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:04.5596000Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:53:04.5596070Z         
2025-03-14T04:53:04.5596370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:04.5596563Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:04.5596674Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:53:04.5596745Z         
2025-03-14T04:53:04.5597078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:04.5597221Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:04.5597282Z         
2025-03-14T04:53:04.5597616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:04.5597748Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:04.5597815Z         
2025-03-14T04:53:04.5598159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:04.5598304Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:04.5598430Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:53:04.5598578Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:04.5598717Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:53:04.5598782Z         
2025-03-14T04:53:04.5599141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:04.5599319Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:04.5599447Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:53:04.5599596Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:04.5599755Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:53:04.5599816Z         
2025-03-14T04:53:04.5600154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:04.5600271Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:04.5600497Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:04.5600637Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:53:04.5600707Z         
2025-03-14T04:53:04.5601040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:04.5601163Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:04.5601329Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:04.5601465Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:53:04.5601532Z         
2025-03-14T04:53:04.5601848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:04.5601946Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:04.5602073Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:04.5602136Z         
2025-03-14T04:53:04.5602451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:04.5602546Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:04.5602672Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:04.5602737Z         
2025-03-14T04:53:04.5603047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:04.5603160Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:04.5603297Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:04.5603362Z         
2025-03-14T04:53:04.5603679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:04.5603792Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:04.5603925Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:04.5603991Z         
2025-03-14T04:53:04.5604343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:04.5604524Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:04.5604597Z         
2025-03-14T04:53:04.5604943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:04.5605103Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:53:04.5605216Z         
2025-03-14T04:53:04.5605596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:53:04.5605790Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:53:04.5605851Z         
2025-03-14T04:53:04.5606342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:53:04.5606504Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:53:04.5606573Z         
2025-03-14T04:53:04.5606868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:04.5607010Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:53:04.5607072Z         
2025-03-14T04:53:04.5607510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:53:04.5607631Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:53:04.5607730Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:53:04.5607848Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:53:04.5607911Z         
2025-03-14T04:53:04.5608381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:53:04.5608546Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:53:04.5608793Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:53:04.5608857Z         
2025-03-14T04:53:04.5609330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:53:04.5609496Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:53:04.5609569Z         
2025-03-14T04:53:04.5609869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:04.5610027Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:53:04.5610089Z         
2025-03-14T04:53:04.5610481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:53:04.5610627Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:53:04.5610696Z         
2025-03-14T04:53:04.5610996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:04.5611146Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:53:04.5611210Z         
2025-03-14T04:53:04.5611696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:53:04.5611842Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:53:04.5611914Z         
2025-03-14T04:53:04.5612405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:53:04.5612569Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:53:04.5612693Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:04.5612879Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:53:04.5613009Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:53:04.5613080Z         
2025-03-14T04:53:04.5613456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:53:04.5613578Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:53:04.5613644Z         
2025-03-14T04:53:04.5614215Z 
2025-03-14T04:53:04.5614309Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:04.5717329Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:53:04.5718179Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:53:04.5718534Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5718954Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5719356Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5719732Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5720111Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5720466Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5720906Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5721282Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5721623Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5721971Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5722280Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5722623Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5722956Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5723290Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5723595Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5723880Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5724212Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5724549Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5724863Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5725175Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5725476Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:04.5725822Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5726176Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5726509Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:04.5726831Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:04.5727121Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5727540Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5727932Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5728359Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5728736Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5729104Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5729500Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5729900Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5730272Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5730626Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5730952Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5731363Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5731856Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5732289Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5732698Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5733039Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5733421Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5733805Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5734152Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5734504Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5734819Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5735204Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5735624Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5735972Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5736345Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5736661Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5737069Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5737433Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5737786Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5738131Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5738451Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5738847Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5739203Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5739518Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5739821Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5740100Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5740428Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5740765Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5741074Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5741381Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5741660Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5741991Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5742356Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5742666Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5742989Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5743280Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:04.5743652Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5743992Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5744329Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:04.5744683Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:04.5744989Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5745365Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5745726Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5746078Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5746411Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5746725Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5747093Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5747449Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5747765Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5748069Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5748353Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5748712Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5749054Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5749379Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5749693Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5749986Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5750329Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5750663Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5750974Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5751286Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5751561Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5751900Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5752227Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5752553Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5752865Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5753141Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5753481Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5753808Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5754126Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5754428Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5754714Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5755079Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5755430Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5755746Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5756047Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5756347Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5756678Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5757011Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5757324Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5757635Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5757912Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5758249Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5758581Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5758890Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5759198Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5759474Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5759808Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5760135Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5760454Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5760892Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5761268Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5761608Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5761957Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5762273Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5762606Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5762889Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5763220Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5763556Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5763864Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5764178Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5764481Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:04.5764833Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5765188Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5765521Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:04.5765854Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:04.5766136Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5766484Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5766817Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5767144Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5767490Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5767773Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5768135Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5768471Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5768814Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5769126Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5769420Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5769763Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5770105Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5770430Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5770743Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5771031Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5771380Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5771808Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5772169Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5772527Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5772832Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5773179Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5773521Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5773840Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5774207Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5774491Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5774856Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5775194Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5775534Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5775849Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5776143Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5776486Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5776820Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5777149Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5777458Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5777752Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5778092Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5778437Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5778762Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5779083Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5779379Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5779720Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5780062Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5780415Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5780731Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5781030Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5781375Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5781726Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5782054Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5782373Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5782659Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5783008Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5783346Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5783673Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5783990Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5784278Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5784615Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5784961Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5785292Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5785602Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5785893Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5786232Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5786578Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5786928Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5787268Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5787551Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5787895Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5788256Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5788570Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5788887Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5789167Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5789506Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5789841Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5790164Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5790478Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5790774Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5791125Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5791468Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5791799Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5792110Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5792398Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5792742Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5793125Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5793445Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5793775Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5794064Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5794419Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5794763Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5795078Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5795395Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5795678Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5796026Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5796374Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5796693Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5797005Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5797288Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5797631Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5797963Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5798292Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5798593Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5798881Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5799220Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5799596Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5799936Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5800244Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5800533Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5800893Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5801234Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5801552Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5801872Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5802159Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5802508Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5802852Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5803172Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5803485Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5803766Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5804110Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5804444Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5804771Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5805081Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5805374Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5805754Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5806089Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5806431Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5806740Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5807051Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5807397Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5807752Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5808091Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5808425Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5808736Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5809098Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5809460Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5809797Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5810145Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5810457Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5810838Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5811200Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5811619Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5811991Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5812371Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5812776Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5813175Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5813520Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5813864Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5814178Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5814542Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5814915Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5815269Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5815596Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5815907Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5816264Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5816638Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5816985Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5817319Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5817620Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5817988Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5818365Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5818706Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5819042Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5819373Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5819750Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5820134Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5820486Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5820842Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5821151Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5821520Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5821896Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5822224Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5822537Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5822833Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5823176Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5823521Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5823841Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5824163Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5824460Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5824804Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5825149Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5825468Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5825826Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5826115Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5826480Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5826813Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5827156Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5827478Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5827765Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5828112Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5828448Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5828777Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5829090Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5829384Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5829721Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5830063Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5830393Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5830705Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5831002Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5831341Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5831685Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5832033Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5832356Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5832659Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5833003Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5833367Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5833689Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5834004Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5834287Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5834632Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5834967Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5835296Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5835604Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5835908Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5836243Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5836575Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5836899Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5837209Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5837499Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5837837Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5838218Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5838542Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5838875Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5839168Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5839506Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5839869Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5840192Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5840509Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5840791Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5841141Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5841480Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5841809Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5842133Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5842417Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5842767Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5843110Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5843443Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5843762Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5844058Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5844406Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5844787Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5845114Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5845444Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5845736Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5846097Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5846442Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5846761Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5847079Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5847361Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5847711Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5848066Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5848402Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5848732Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5849037Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5849404Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5849760Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5850108Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5850431Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5850740Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5851135Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5851545Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5851917Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5852247Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5852572Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5852938Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5853281Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5853626Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5853952Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5854262Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5854622Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5854999Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5855354Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5855687Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5855993Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5856376Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5856754Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5857102Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5857456Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5857809Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5858181Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5858561Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5858920Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5859273Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5859585Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5859954Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5860325Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5860785Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5861120Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5861435Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5861794Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5862179Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5862541Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5862902Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5863236Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5863620Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5864017Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5864382Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5864741Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5865109Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5865505Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5865858Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5866218Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5866601Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5866932Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5867303Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5867659Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5868010Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5868341Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5868655Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5869022Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5869388Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5869732Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5870062Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5870372Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5870734Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5871098Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5871447Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5871840Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5872161Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5872568Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5872960Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5873357Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5873715Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5874040Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5874428Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5874804Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5875171Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5875522Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5875850Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5876242Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5876620Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5876987Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5877335Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5877663Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5878043Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5878438Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5878827Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5879185Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5879531Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5879912Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5880307Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5880658Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5881009Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5881330Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5881693Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5882045Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5882386Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5882717Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5883031Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:04.5883405Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5883769Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5884125Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:04.5884467Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:04.5884769Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5885134Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5885544Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5885908Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5886285Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5886615Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5886996Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5887401Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5887757Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5888109Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5888423Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5888808Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5889204Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5889564Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5889917Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5890233Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:04.5890613Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5890997Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5891357Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.5891752Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.5892078Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:04.5892468Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5892878Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5893222Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.5893565Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.5893875Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:04.5894247Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.5894609Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.5894947Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.5895281Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.5895658Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:53:04.5895995Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:53:04.5896325Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:53:04.5896696Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:53:04.5897089Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:53:04.5897460Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:53:04.5897809Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:53:04.5897876Z         
2025-03-14T04:53:04.5898178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5898662Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5898725Z         
2025-03-14T04:53:04.5899016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5900515Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5900601Z         
2025-03-14T04:53:04.5900908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:53:04.5901058Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:53:04.5901121Z         
2025-03-14T04:53:04.5901495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:53:04.5901738Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:53:04.5901807Z         
2025-03-14T04:53:04.5902072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5902500Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5902571Z         
2025-03-14T04:53:04.5902843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5904392Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5904463Z         
2025-03-14T04:53:04.5904753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5904898Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:53:04.5904960Z         
2025-03-14T04:53:04.5905221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5905651Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5905720Z         
2025-03-14T04:53:04.5905987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5907578Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5907681Z         
2025-03-14T04:53:04.5907972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5908121Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:53:04.5908183Z         
2025-03-14T04:53:04.5908437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5908878Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5908947Z         
2025-03-14T04:53:04.5909209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5910749Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5910821Z         
2025-03-14T04:53:04.5911070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5911516Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:04.5911579Z         
2025-03-14T04:53:04.5911845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5913492Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5913578Z         
2025-03-14T04:53:04.5913866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5914011Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:53:04.5914095Z         
2025-03-14T04:53:04.5914381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5914540Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:53:04.5914602Z         
2025-03-14T04:53:04.5914857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5915278Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5915346Z         
2025-03-14T04:53:04.5915610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5917166Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5917236Z         
2025-03-14T04:53:04.5917523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5917672Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:53:04.5917737Z         
2025-03-14T04:53:04.5917995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5918422Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5918491Z         
2025-03-14T04:53:04.5918751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5920340Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5920428Z         
2025-03-14T04:53:04.5920723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5920887Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:53:04.5920948Z         
2025-03-14T04:53:04.5921205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5921634Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5921705Z         
2025-03-14T04:53:04.5921966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5923540Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5923610Z         
2025-03-14T04:53:04.5923889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5924048Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:53:04.5924108Z         
2025-03-14T04:53:04.5924408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5924562Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:53:04.5924629Z         
2025-03-14T04:53:04.5924875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5925311Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5925372Z         
2025-03-14T04:53:04.5925649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5927197Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5927293Z         
2025-03-14T04:53:04.5927578Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5927717Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:53:04.5927788Z         
2025-03-14T04:53:04.5928043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5928482Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5928549Z         
2025-03-14T04:53:04.5928824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5930403Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5930472Z         
2025-03-14T04:53:04.5930768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5930910Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:53:04.5930984Z         
2025-03-14T04:53:04.5931238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5931725Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5931793Z         
2025-03-14T04:53:04.5932064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5933741Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5933819Z         
2025-03-14T04:53:04.5934106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5934274Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:53:04.5934354Z         
2025-03-14T04:53:04.5934632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5934786Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:53:04.5934848Z         
2025-03-14T04:53:04.5935098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5935509Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5935578Z         
2025-03-14T04:53:04.5935833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5937362Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5937435Z         
2025-03-14T04:53:04.5937720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5937866Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:53:04.5937928Z         
2025-03-14T04:53:04.5938182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5938621Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5938688Z         
2025-03-14T04:53:04.5938958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5940545Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5940647Z         
2025-03-14T04:53:04.5940938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5941088Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:53:04.5941148Z         
2025-03-14T04:53:04.5941395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5941833Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5941895Z         
2025-03-14T04:53:04.5942165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5943709Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5943782Z         
2025-03-14T04:53:04.5944031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5944487Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:04.5944555Z         
2025-03-14T04:53:04.5944812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5946453Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5946531Z         
2025-03-14T04:53:04.5946821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5946966Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:53:04.5947053Z         
2025-03-14T04:53:04.5947337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5947502Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:53:04.5947578Z         
2025-03-14T04:53:04.5947835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5948269Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5948335Z         
2025-03-14T04:53:04.5948612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5950149Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5950225Z         
2025-03-14T04:53:04.5950521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5950666Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:53:04.5950738Z         
2025-03-14T04:53:04.5950992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5951430Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5951496Z         
2025-03-14T04:53:04.5951771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5953321Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5953408Z         
2025-03-14T04:53:04.5953699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5953853Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:53:04.5953919Z         
2025-03-14T04:53:04.5954166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5954600Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5954662Z         
2025-03-14T04:53:04.5954929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5956493Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5956567Z         
2025-03-14T04:53:04.5956848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5957003Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:53:04.5957071Z         
2025-03-14T04:53:04.5957349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5957506Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:53:04.5957566Z         
2025-03-14T04:53:04.5957820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5958239Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5958307Z         
2025-03-14T04:53:04.5958572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5960185Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5960293Z         
2025-03-14T04:53:04.5960663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5960823Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:53:04.5960886Z         
2025-03-14T04:53:04.5961143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5961576Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5961647Z         
2025-03-14T04:53:04.5961909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5963446Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5963518Z         
2025-03-14T04:53:04.5963800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5963945Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:53:04.5964009Z         
2025-03-14T04:53:04.5964264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5964691Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5964763Z         
2025-03-14T04:53:04.5965024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5966687Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5966784Z         
2025-03-14T04:53:04.5967063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5967246Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:53:04.5967308Z         
2025-03-14T04:53:04.5967596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5967742Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:53:04.5967812Z         
2025-03-14T04:53:04.5968060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5968486Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5968547Z         
2025-03-14T04:53:04.5968822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5970399Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5970465Z         
2025-03-14T04:53:04.5970763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5970907Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:53:04.5970977Z         
2025-03-14T04:53:04.5971237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5971744Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5971817Z         
2025-03-14T04:53:04.5972101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5973701Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5973794Z         
2025-03-14T04:53:04.5974091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5974233Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:53:04.5974304Z         
2025-03-14T04:53:04.5974556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5975001Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5975064Z         
2025-03-14T04:53:04.5975350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5978644Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5978722Z         
2025-03-14T04:53:04.5979020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5979180Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:53:04.5979250Z         
2025-03-14T04:53:04.5979544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5979704Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:53:04.5979767Z         
2025-03-14T04:53:04.5980031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5980498Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5980565Z         
2025-03-14T04:53:04.5980863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5982427Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5982540Z         
2025-03-14T04:53:04.5982838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5982976Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:53:04.5983049Z         
2025-03-14T04:53:04.5983310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5983761Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5983826Z         
2025-03-14T04:53:04.5984108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5985793Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5985861Z         
2025-03-14T04:53:04.5986158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5986294Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:53:04.5986366Z         
2025-03-14T04:53:04.5986619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5987067Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5987132Z         
2025-03-14T04:53:04.5987410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5989015Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5989117Z         
2025-03-14T04:53:04.5989381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5989836Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:04.5989906Z         
2025-03-14T04:53:04.5990171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5991824Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5991897Z         
2025-03-14T04:53:04.5992184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.5992350Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:53:04.5992412Z         
2025-03-14T04:53:04.5992692Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5992828Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:53:04.5992895Z         
2025-03-14T04:53:04.5993132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5993546Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.5993612Z         
2025-03-14T04:53:04.5993871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5995393Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5995481Z         
2025-03-14T04:53:04.5995767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5995897Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:53:04.5995964Z         
2025-03-14T04:53:04.5996202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5996621Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.5996690Z         
2025-03-14T04:53:04.5996941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.5998433Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.5998495Z         
2025-03-14T04:53:04.5998795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.5998922Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:53:04.5998987Z         
2025-03-14T04:53:04.5999227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.5999642Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.5999712Z         
2025-03-14T04:53:04.5999971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6001498Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6001578Z         
2025-03-14T04:53:04.6001854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6002021Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:53:04.6002079Z         
2025-03-14T04:53:04.6002360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6002495Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:53:04.6002560Z         
2025-03-14T04:53:04.6002798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6003204Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6003263Z         
2025-03-14T04:53:04.6003523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6005040Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6005112Z         
2025-03-14T04:53:04.6005393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6005520Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:53:04.6005587Z         
2025-03-14T04:53:04.6005829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6006243Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6006306Z         
2025-03-14T04:53:04.6006568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6008111Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6008213Z         
2025-03-14T04:53:04.6008502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6008631Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:53:04.6008700Z         
2025-03-14T04:53:04.6008943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6009376Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6009443Z         
2025-03-14T04:53:04.6009732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6011350Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6011479Z         
2025-03-14T04:53:04.6011834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6012004Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:53:04.6012085Z         
2025-03-14T04:53:04.6012410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6012579Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:53:04.6012651Z         
2025-03-14T04:53:04.6012943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6013414Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6013489Z         
2025-03-14T04:53:04.6013776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6015345Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6015451Z         
2025-03-14T04:53:04.6015736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6015874Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:53:04.6015934Z         
2025-03-14T04:53:04.6016188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6016610Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6016680Z         
2025-03-14T04:53:04.6016945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6018491Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6018562Z         
2025-03-14T04:53:04.6018847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6018985Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:53:04.6019046Z         
2025-03-14T04:53:04.6019302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6019728Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6019796Z         
2025-03-14T04:53:04.6020060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6021651Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6021749Z         
2025-03-14T04:53:04.6022025Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6022174Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:53:04.6022237Z         
2025-03-14T04:53:04.6022523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6022661Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:53:04.6022732Z         
2025-03-14T04:53:04.6022976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6023398Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6023459Z         
2025-03-14T04:53:04.6023730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6025287Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6025352Z         
2025-03-14T04:53:04.6025655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6025781Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:53:04.6025849Z         
2025-03-14T04:53:04.6026094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6026515Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6026577Z         
2025-03-14T04:53:04.6026844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6028345Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6028433Z         
2025-03-14T04:53:04.6028720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6028848Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:53:04.6028918Z         
2025-03-14T04:53:04.6029165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6029596Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6029659Z         
2025-03-14T04:53:04.6029929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6031453Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6031515Z         
2025-03-14T04:53:04.6031789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6031928Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:53:04.6031994Z         
2025-03-14T04:53:04.6032264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6032407Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:53:04.6032467Z         
2025-03-14T04:53:04.6032717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6033131Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6033202Z         
2025-03-14T04:53:04.6033480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6035004Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6035115Z         
2025-03-14T04:53:04.6035393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6035531Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:53:04.6035595Z         
2025-03-14T04:53:04.6035843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6036254Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6036322Z         
2025-03-14T04:53:04.6036598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6038124Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6038189Z         
2025-03-14T04:53:04.6038470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6038594Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:53:04.6038662Z         
2025-03-14T04:53:04.6038903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6039324Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6039385Z         
2025-03-14T04:53:04.6039646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6041210Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6041301Z         
2025-03-14T04:53:04.6041581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6041721Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:53:04.6041789Z         
2025-03-14T04:53:04.6042063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6042206Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:53:04.6042267Z         
2025-03-14T04:53:04.6042515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6042927Z         x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6042999Z         
2025-03-14T04:53:04.6043265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6044827Z         x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6044899Z         
2025-03-14T04:53:04.6045182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6045322Z         out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:53:04.6045384Z         
2025-03-14T04:53:04.6045641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6046064Z         x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6046133Z         
2025-03-14T04:53:04.6046415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6047994Z         x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6048098Z         
2025-03-14T04:53:04.6048400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6048548Z         out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:53:04.6048614Z         
2025-03-14T04:53:04.6048914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6049385Z         x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6049455Z         
2025-03-14T04:53:04.6049734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6051355Z         x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6051478Z         
2025-03-14T04:53:04.6051808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6051977Z         x_93 += out_51;  out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:53:04.6052046Z         
2025-03-14T04:53:04.6052373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6052531Z         out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:53:04.6052607Z         
2025-03-14T04:53:04.6052889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6053331Z         x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6053401Z         
2025-03-14T04:53:04.6053665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6055226Z         x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6055304Z         
2025-03-14T04:53:04.6055599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6055733Z         out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:53:04.6055802Z         
2025-03-14T04:53:04.6056051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6056481Z         x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6056550Z         
2025-03-14T04:53:04.6056812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6058377Z         x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6058440Z         
2025-03-14T04:53:04.6058731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6058861Z         out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:53:04.6058930Z         
2025-03-14T04:53:04.6059175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6059614Z         x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6059683Z         
2025-03-14T04:53:04.6059966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6061675Z         x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6083354Z         
2025-03-14T04:53:04.6083732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6083892Z         x_99 += out_55;  out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:53:04.6083965Z         
2025-03-14T04:53:04.6084259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6084409Z         out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:53:04.6084475Z         
2025-03-14T04:53:04.6084742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6085180Z         x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6085251Z         
2025-03-14T04:53:04.6085525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6087153Z         x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6087231Z         
2025-03-14T04:53:04.6087519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6087668Z         out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:53:04.6087731Z         
2025-03-14T04:53:04.6087991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6088462Z         x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6088536Z         
2025-03-14T04:53:04.6088807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6090439Z         x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6090528Z         
2025-03-14T04:53:04.6090828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6090982Z         out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:53:04.6091048Z         
2025-03-14T04:53:04.6091323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6091896Z         x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6091982Z         
2025-03-14T04:53:04.6092278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6093957Z         x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6094031Z         
2025-03-14T04:53:04.6094316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6094476Z         x_105 += out_59;  out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:53:04.6094537Z         
2025-03-14T04:53:04.6094833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6094973Z         out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:53:04.6095046Z         
2025-03-14T04:53:04.6095299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6095742Z         x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6095822Z         
2025-03-14T04:53:04.6096095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6097642Z         x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6097722Z         
2025-03-14T04:53:04.6098012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6098145Z         out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:53:04.6098215Z         
2025-03-14T04:53:04.6098461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6098894Z         x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6098959Z         
2025-03-14T04:53:04.6099231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6100804Z         x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6100872Z         
2025-03-14T04:53:04.6101167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6101305Z         out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:53:04.6101378Z         
2025-03-14T04:53:04.6101633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6102089Z         x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6102153Z         
2025-03-14T04:53:04.6102425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6103994Z         x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6104073Z         
2025-03-14T04:53:04.6104362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6104510Z         x_111 += out_63;  out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:53:04.6104582Z         
2025-03-14T04:53:04.6104863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6105011Z         out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:53:04.6105073Z         
2025-03-14T04:53:04.6105333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6105755Z         x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6105828Z         
2025-03-14T04:53:04.6106098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6107662Z         x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6107738Z         
2025-03-14T04:53:04.6108025Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6108169Z         out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:53:04.6108229Z         
2025-03-14T04:53:04.6108487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6108962Z         x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6109048Z         
2025-03-14T04:53:04.6109321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6110868Z         x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6110951Z         
2025-03-14T04:53:04.6111237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6111380Z         out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:53:04.6111442Z         
2025-03-14T04:53:04.6111699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6112136Z         x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6112199Z         
2025-03-14T04:53:04.6112467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6114042Z         x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6114117Z         
2025-03-14T04:53:04.6114400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6114547Z         x_117 += out_67;  out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:53:04.6114619Z         
2025-03-14T04:53:04.6114897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6115041Z         out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:53:04.6115118Z         
2025-03-14T04:53:04.6115375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6115810Z         x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6115878Z         
2025-03-14T04:53:04.6116141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6117714Z         x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6117784Z         
2025-03-14T04:53:04.6118066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6118206Z         out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:53:04.6118266Z         
2025-03-14T04:53:04.6118520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6118940Z         x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6119009Z         
2025-03-14T04:53:04.6119268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6120832Z         x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6120905Z         
2025-03-14T04:53:04.6121189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6121330Z         out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:53:04.6121389Z         
2025-03-14T04:53:04.6121657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6122084Z         x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6122169Z         
2025-03-14T04:53:04.6122431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6123993Z         x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6124078Z         
2025-03-14T04:53:04.6124353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6124512Z         x_123 += out_71;  out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:53:04.6124573Z         
2025-03-14T04:53:04.6124857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6124996Z         out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:53:04.6125061Z         
2025-03-14T04:53:04.6125307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6125729Z         x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6125790Z         
2025-03-14T04:53:04.6126076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6127635Z         x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6127700Z         
2025-03-14T04:53:04.6127985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6128131Z         out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:53:04.6128200Z         
2025-03-14T04:53:04.6128445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6128889Z         x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6128950Z         
2025-03-14T04:53:04.6129219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6130787Z         x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6130852Z         
2025-03-14T04:53:04.6131149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6131300Z         out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:53:04.6131379Z         
2025-03-14T04:53:04.6131737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6132244Z         x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6132318Z         
2025-03-14T04:53:04.6132650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6134155Z         x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6134218Z         
2025-03-14T04:53:04.6134502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6134649Z         x_129 += out_75;  out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:53:04.6134720Z         
2025-03-14T04:53:04.6135023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6135172Z         out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:53:04.6135260Z         
2025-03-14T04:53:04.6135523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6135923Z         x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6136008Z         
2025-03-14T04:53:04.6136266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6137774Z         x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6137844Z         
2025-03-14T04:53:04.6138128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6138265Z         out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:53:04.6138325Z         
2025-03-14T04:53:04.6138570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6138977Z         x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6139060Z         
2025-03-14T04:53:04.6139322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6140817Z         x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6140887Z         
2025-03-14T04:53:04.6141166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6141317Z         out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:53:04.6141376Z         
2025-03-14T04:53:04.6141625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6142055Z         x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6142120Z         
2025-03-14T04:53:04.6142393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6143903Z         x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6143971Z         
2025-03-14T04:53:04.6144240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6144390Z         x_135 += out_79;  out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:53:04.6144449Z         
2025-03-14T04:53:04.6144729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6144865Z         out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:53:04.6144935Z         
2025-03-14T04:53:04.6145181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6145623Z         x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6145691Z         
2025-03-14T04:53:04.6145946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6147448Z         x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6147524Z         
2025-03-14T04:53:04.6147809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6147956Z         out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:53:04.6148023Z         
2025-03-14T04:53:04.6148270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6148699Z         x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6148783Z         
2025-03-14T04:53:04.6149056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6150653Z         x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6150716Z         
2025-03-14T04:53:04.6151004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6151138Z         out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:53:04.6151200Z         
2025-03-14T04:53:04.6151451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6151893Z         x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6151965Z         
2025-03-14T04:53:04.6152228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6153781Z         x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6153850Z         
2025-03-14T04:53:04.6154142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6154292Z         x_141 += out_83;  out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:53:04.6154369Z         
2025-03-14T04:53:04.6154653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6154790Z         out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:53:04.6154858Z         
2025-03-14T04:53:04.6155108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6155549Z         x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6155610Z         
2025-03-14T04:53:04.6155880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6157427Z         x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6157497Z         
2025-03-14T04:53:04.6157783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6157917Z         out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:53:04.6157984Z         
2025-03-14T04:53:04.6158233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6158689Z         x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6158753Z         
2025-03-14T04:53:04.6159019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6160707Z         x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6160790Z         
2025-03-14T04:53:04.6161083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6161239Z         out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:53:04.6161307Z         
2025-03-14T04:53:04.6161555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6161991Z         x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6162079Z         
2025-03-14T04:53:04.6162353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6163867Z         x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6163940Z         
2025-03-14T04:53:04.6164212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6164354Z         x_147 += out_87;  out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:53:04.6164420Z         
2025-03-14T04:53:04.6164689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6164829Z         out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:53:04.6164909Z         
2025-03-14T04:53:04.6165157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6165562Z         x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6165631Z         
2025-03-14T04:53:04.6165887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6167451Z         x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6167535Z         
2025-03-14T04:53:04.6167815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6167952Z         out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:53:04.6168011Z         
2025-03-14T04:53:04.6168265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6168730Z         x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6168802Z         
2025-03-14T04:53:04.6169081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6170794Z         x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6170871Z         
2025-03-14T04:53:04.6171182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6171334Z         out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:53:04.6171401Z         
2025-03-14T04:53:04.6171764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6172254Z         x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6172331Z         
2025-03-14T04:53:04.6172625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6174234Z         x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6174307Z         
2025-03-14T04:53:04.6174576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6174748Z         x_153 += out_91;  out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:53:04.6174808Z         
2025-03-14T04:53:04.6175092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6175246Z         out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:53:04.6175317Z         
2025-03-14T04:53:04.6175562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6175980Z         x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6176041Z         
2025-03-14T04:53:04.6176306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6177859Z         x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6177925Z         
2025-03-14T04:53:04.6178213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6178357Z         out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:53:04.6178425Z         
2025-03-14T04:53:04.6178670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6179108Z         x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6179169Z         
2025-03-14T04:53:04.6179435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6181040Z         x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6181121Z         
2025-03-14T04:53:04.6181432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6181559Z         out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:53:04.6181649Z         
2025-03-14T04:53:04.6181894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6182328Z         x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6182394Z         
2025-03-14T04:53:04.6182654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6184200Z         x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6184263Z         
2025-03-14T04:53:04.6184545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6184690Z         x_159 += out_95;  out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:53:04.6184756Z         
2025-03-14T04:53:04.6185051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6185201Z         out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:53:04.6185261Z         
2025-03-14T04:53:04.6185515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6185940Z         x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6186003Z         
2025-03-14T04:53:04.6186273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6187842Z         x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6187931Z         
2025-03-14T04:53:04.6188214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6188376Z         out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:53:04.6188443Z         
2025-03-14T04:53:04.6188690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6189134Z         x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6189200Z         
2025-03-14T04:53:04.6189482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6191109Z         x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6191180Z         
2025-03-14T04:53:04.6191466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6191620Z         out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:53:04.6191688Z         
2025-03-14T04:53:04.6191938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6192378Z         x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6192440Z         
2025-03-14T04:53:04.6192713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6194337Z         x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6194426Z         
2025-03-14T04:53:04.6194722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6194883Z         x_165 += out_99;  out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:53:04.6194968Z         
2025-03-14T04:53:04.6195269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6195418Z         out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:53:04.6195477Z         
2025-03-14T04:53:04.6195733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6196151Z         x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6196218Z         
2025-03-14T04:53:04.6196481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6198036Z         x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6198105Z         
2025-03-14T04:53:04.6198403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6198544Z         out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:53:04.6198605Z         
2025-03-14T04:53:04.6198858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6199282Z         x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6199350Z         
2025-03-14T04:53:04.6199611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6201181Z         x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6201265Z         
2025-03-14T04:53:04.6201549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6201707Z         out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:53:04.6201768Z         
2025-03-14T04:53:04.6202022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6202455Z         x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6202524Z         
2025-03-14T04:53:04.6202792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6204372Z         x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6204446Z         
2025-03-14T04:53:04.6204759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6204934Z         x_171 += out_103;  out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:53:04.6204998Z         
2025-03-14T04:53:04.6205299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6205450Z         out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:53:04.6205521Z         
2025-03-14T04:53:04.6205781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6206226Z         x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6206290Z         
2025-03-14T04:53:04.6206570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6208230Z         x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6208332Z         
2025-03-14T04:53:04.6208634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6208776Z         out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:53:04.6208849Z         
2025-03-14T04:53:04.6209123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6209608Z         x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6209676Z         
2025-03-14T04:53:04.6209975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6211834Z         x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6211915Z         
2025-03-14T04:53:04.6212252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6212406Z         out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:53:04.6212492Z         
2025-03-14T04:53:04.6212752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6213216Z         x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6213280Z         
2025-03-14T04:53:04.6213564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6215223Z         x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6215322Z         
2025-03-14T04:53:04.6215625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6215790Z         x_177 += out_107;  out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:53:04.6215864Z         
2025-03-14T04:53:04.6216161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6216324Z         out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:53:04.6216391Z         
2025-03-14T04:53:04.6216658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6217102Z         x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6217174Z         
2025-03-14T04:53:04.6217456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6219540Z         x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6219623Z         
2025-03-14T04:53:04.6219926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6220076Z         out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:53:04.6220142Z         
2025-03-14T04:53:04.6220415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6220863Z         x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6220938Z         
2025-03-14T04:53:04.6221224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6222933Z         x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6223028Z         
2025-03-14T04:53:04.6223312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6223449Z         out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:53:04.6223511Z         
2025-03-14T04:53:04.6223767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6224205Z         x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6224266Z         
2025-03-14T04:53:04.6224537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6226131Z         x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6226204Z         
2025-03-14T04:53:04.6226483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6226645Z         x_183 += out_111;  out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:53:04.6226715Z         
2025-03-14T04:53:04.6226994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6227140Z         out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:53:04.6227202Z         
2025-03-14T04:53:04.6227457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6227878Z         x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6227964Z         
2025-03-14T04:53:04.6228230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6229796Z         x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6229879Z         
2025-03-14T04:53:04.6230160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6230300Z         out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:53:04.6230361Z         
2025-03-14T04:53:04.6230614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6231042Z         x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6231111Z         
2025-03-14T04:53:04.6231372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6232943Z         x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6233015Z         
2025-03-14T04:53:04.6233294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6233431Z         out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:53:04.6233491Z         
2025-03-14T04:53:04.6233741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6234165Z         x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6234233Z         
2025-03-14T04:53:04.6234523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6236081Z         x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6236180Z         
2025-03-14T04:53:04.6236456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6236613Z         x_189 += out_115;  out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:53:04.6236676Z         
2025-03-14T04:53:04.6236959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6237099Z         out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:53:04.6237168Z         
2025-03-14T04:53:04.6237416Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6237850Z         x_190: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6237909Z         
2025-03-14T04:53:04.6238170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6239695Z         x_191: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_190 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6239764Z         
2025-03-14T04:53:04.6240042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6240172Z         out_120: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_191);  x_191 = None
2025-03-14T04:53:04.6240237Z         
2025-03-14T04:53:04.6240476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6240915Z         x_192: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_120 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6240977Z         
2025-03-14T04:53:04.6241238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6242747Z         x_193: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_192 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6242823Z         
2025-03-14T04:53:04.6243108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6243240Z         out_121: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_193);  x_193 = None
2025-03-14T04:53:04.6243307Z         
2025-03-14T04:53:04.6243546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6243976Z         x_194: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_121 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6244038Z         
2025-03-14T04:53:04.6244301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6245816Z         x_195: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_194 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6245879Z         
2025-03-14T04:53:04.6246131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6246563Z         x_196: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_119 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:04.6246632Z         
2025-03-14T04:53:04.6246896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6248544Z         x_197: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_196 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6248642Z         
2025-03-14T04:53:04.6248917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6249074Z         x_195 += x_197;  out_122: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_195;  x_195 = x_197 = None
2025-03-14T04:53:04.6249136Z         
2025-03-14T04:53:04.6249417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6249567Z         out_123: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_122);  out_122 = None
2025-03-14T04:53:04.6249636Z         
2025-03-14T04:53:04.6249879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6250300Z         x_198: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6250361Z         
2025-03-14T04:53:04.6250627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6252375Z         x_199: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_198 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6252455Z         
2025-03-14T04:53:04.6252780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6252916Z         out_124: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_199);  x_199 = None
2025-03-14T04:53:04.6252988Z         
2025-03-14T04:53:04.6253248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6253673Z         x_200: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_124 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6253739Z         
2025-03-14T04:53:04.6254045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6255808Z         x_201: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_200 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6255908Z         
2025-03-14T04:53:04.6256235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6256384Z         out_125: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_201);  x_201 = None
2025-03-14T04:53:04.6256461Z         
2025-03-14T04:53:04.6256741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6257231Z         x_202: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_125 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6257300Z         
2025-03-14T04:53:04.6257606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6259366Z         x_203: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_202 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6259441Z         
2025-03-14T04:53:04.6259758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6259934Z         x_203 += out_123;  out_126: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_203;  x_203 = out_123 = None
2025-03-14T04:53:04.6260009Z         
2025-03-14T04:53:04.6260325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6260492Z         out_127: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_126);  out_126 = None
2025-03-14T04:53:04.6260706Z         
2025-03-14T04:53:04.6261000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6261458Z         x_204: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6261559Z         
2025-03-14T04:53:04.6261825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6263363Z         x_205: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_204 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6263455Z         
2025-03-14T04:53:04.6263728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6263865Z         out_128: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_205);  x_205 = None
2025-03-14T04:53:04.6263925Z         
2025-03-14T04:53:04.6264172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6264596Z         x_206: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_128 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6264660Z         
2025-03-14T04:53:04.6264924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6266434Z         x_207: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_206 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6266507Z         
2025-03-14T04:53:04.6266779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6266917Z         out_129: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_207);  x_207 = None
2025-03-14T04:53:04.6266982Z         
2025-03-14T04:53:04.6267218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6267639Z         x_208: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_129 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6267700Z         
2025-03-14T04:53:04.6267973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6269469Z         x_209: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_208 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6269569Z         
2025-03-14T04:53:04.6269848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6270001Z         x_209 += out_127;  out_130: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_209;  x_209 = out_127 = None
2025-03-14T04:53:04.6270070Z         
2025-03-14T04:53:04.6270340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6270488Z         out_131: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_130);  out_130 = None
2025-03-14T04:53:04.6270548Z         
2025-03-14T04:53:04.6270986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:04.6271134Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:53:04.6271202Z         
2025-03-14T04:53:04.6271489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:04.6271628Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:53:04.6271688Z         
2025-03-14T04:53:04.6272122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:04.6272280Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:53:04.6272350Z         
2025-03-14T04:53:04.6272639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:04.6272776Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:53:04.6272837Z         
2025-03-14T04:53:04.6273209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:53:04.6273387Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:53:04.6273492Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:53:04.6273612Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:53:04.6273680Z         
2025-03-14T04:53:04.6274027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:53:04.6274155Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:53:04.6274227Z         
2025-03-14T04:53:04.6274548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:53:04.6274683Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:53:04.6274752Z         
2025-03-14T04:53:04.6275117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:53:04.6275332Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:53:04.6275407Z         
2025-03-14T04:53:04.6275824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:53:04.6275943Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:53:04.6276370Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:53:04.6276491Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:53:04.6276602Z         x_210: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:53:04.6276669Z         
2025-03-14T04:53:04.6276965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:04.6277092Z         tensor: "f32[82125, 4][4, 1]cpu" = x_210.to(torch.float32);  x_210 = None
2025-03-14T04:53:04.6277150Z         
2025-03-14T04:53:04.6277404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6278173Z         x_211: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_131, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_131 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:53:04.6278242Z         
2025-03-14T04:53:04.6278526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:53:04.6278721Z         x_212: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_211, inplace = False);  x_211 = None
2025-03-14T04:53:04.6278784Z         
2025-03-14T04:53:04.6279161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:53:04.6279996Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:53:04.6280064Z         
2025-03-14T04:53:04.6280417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:53:04.6281226Z         x_213: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_212 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:53:04.6281304Z         
2025-03-14T04:53:04.6281633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:53:04.6281808Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:53:04.6281941Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:53:04.6282009Z         
2025-03-14T04:53:04.6282411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:53:04.6282572Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_213.view(4, -1, 4, 73, 75);  x_213 = None
2025-03-14T04:53:04.6282736Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:53:04.6282913Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:53:04.6282972Z         
2025-03-14T04:53:04.6283366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:53:04.6283570Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:53:04.6283628Z         
2025-03-14T04:53:04.6284054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:53:04.6284195Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:53:04.6284340Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:53:04.6284487Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:53:04.6284553Z         
2025-03-14T04:53:04.6284922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:04.6285096Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:53:04.6285154Z         
2025-03-14T04:53:04.6285469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:04.6285604Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:53:04.6285669Z         
2025-03-14T04:53:04.6285979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:04.6286113Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:04.6286233Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:04.6286394Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:53:04.6286456Z         
2025-03-14T04:53:04.6286773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:04.6286903Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:04.6287022Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:04.6287162Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:04.6287232Z         
2025-03-14T04:53:04.6287539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:04.6287680Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:04.6287766Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:53:04.6287893Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:53:04.6287955Z         
2025-03-14T04:53:04.6288268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:04.6288409Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:04.6288504Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:53:04.6288628Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:53:04.6288696Z         
2025-03-14T04:53:04.6289052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:04.6289209Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:04.6289323Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:53:04.6289391Z         
2025-03-14T04:53:04.6293321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:04.6293514Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:04.6293626Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:53:04.6293701Z         
2025-03-14T04:53:04.6294058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:04.6294222Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:04.6294343Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:53:04.6294409Z         
2025-03-14T04:53:04.6294722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:04.6294906Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:04.6295023Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:53:04.6295084Z         
2025-03-14T04:53:04.6295430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:04.6295572Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:04.6295642Z         
2025-03-14T04:53:04.6295993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:04.6296132Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:04.6296194Z         
2025-03-14T04:53:04.6296565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:04.6296702Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:04.6296828Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:53:04.6296980Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:04.6297139Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:53:04.6297200Z         
2025-03-14T04:53:04.6297558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:04.6297694Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:04.6297819Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:53:04.6297964Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:04.6298103Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:53:04.6298164Z         
2025-03-14T04:53:04.6298496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:04.6298613Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:04.6298778Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:04.6298906Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:53:04.6298976Z         
2025-03-14T04:53:04.6299305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:04.6299424Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:04.6299586Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:04.6299738Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:53:04.6299802Z         
2025-03-14T04:53:04.6300126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:04.6300222Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:04.6300347Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:04.6300406Z         
2025-03-14T04:53:04.6300727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:04.6300819Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:04.6300933Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:04.6300992Z         
2025-03-14T04:53:04.6301310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:04.6301424Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:04.6301571Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:04.6301630Z         
2025-03-14T04:53:04.6301934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:04.6302057Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:04.6302181Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:04.6302241Z         
2025-03-14T04:53:04.6302593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:04.6302790Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:04.6302851Z         
2025-03-14T04:53:04.6303185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:04.6303344Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:53:04.6303413Z         
2025-03-14T04:53:04.6303797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:53:04.6303973Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:53:04.6304033Z         
2025-03-14T04:53:04.6304526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:53:04.6304659Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:53:04.6304726Z         
2025-03-14T04:53:04.6305018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:04.6305159Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:53:04.6305219Z         
2025-03-14T04:53:04.6305657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:53:04.6305781Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:53:04.6305891Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:53:04.6305999Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:53:04.6306066Z         
2025-03-14T04:53:04.6306526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:53:04.6306693Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:53:04.6306920Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:53:04.6306988Z         
2025-03-14T04:53:04.6307438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:53:04.6307613Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:53:04.6307674Z         
2025-03-14T04:53:04.6308001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:04.6308153Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:53:04.6308235Z         
2025-03-14T04:53:04.6308617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:53:04.6308768Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:53:04.6308830Z         
2025-03-14T04:53:04.6309146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:04.6309294Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:53:04.6309356Z         
2025-03-14T04:53:04.6309737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:53:04.6309872Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:53:04.6309940Z         
2025-03-14T04:53:04.6310418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:53:04.6310557Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:53:04.6310672Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:04.6310829Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:53:04.6310957Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:53:04.6311025Z         
2025-03-14T04:53:04.6311391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:53:04.6311512Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:53:04.6311573Z         
2025-03-14T04:53:04.6311582Z 
2025-03-14T04:53:04.6311673Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:04.6414565Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:53:04.6415320Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:53:04.6415671Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6416088Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6416462Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6416906Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6417297Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6417647Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6418056Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6418472Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6418872Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6419261Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6419614Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6420007Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6420403Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6420778Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6421128Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6421427Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6421837Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6422228Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6422601Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6422934Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6423258Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:04.6423620Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6423997Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6424344Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:04.6424690Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:04.6424983Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6425349Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6425691Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6426023Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6426335Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6426625Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6426965Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6427354Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6427721Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6428051Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6428355Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6428703Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6429052Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6429379Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6429696Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6429980Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6430370Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6430711Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6431051Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6431372Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6431677Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6432025Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6432363Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6432690Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6433002Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6433294Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6433634Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6434011Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6434338Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6434672Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6434967Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6435309Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6435687Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6436009Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6436344Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6436631Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6436990Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6437339Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6437672Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6438000Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6438302Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6438675Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6439023Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6439352Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6439699Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6440020Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:04.6440387Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6440738Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6441093Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:04.6441443Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:04.6441730Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6442063Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6442407Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6442725Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6443045Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6443346Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6443677Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6444024Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6444335Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6444663Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6444938Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6445276Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6445604Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6445920Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6446231Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6446513Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6446847Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6447175Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6447519Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6447830Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6448122Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6448461Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6448804Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6449129Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6449440Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6449743Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6450084Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6450438Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6450755Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6451090Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6451374Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6451786Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6452131Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6452450Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6452770Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6453056Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6453426Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6453780Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6454147Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6454478Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6454789Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6455134Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6455463Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6455791Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6456115Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6456411Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6456765Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6457113Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6457451Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6457787Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6458113Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6458493Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6458876Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6459233Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6459589Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6459905Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6460288Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6460771Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6461124Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6461463Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6461782Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:04.6462165Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6462513Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6462908Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:04.6463260Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:04.6463578Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6463922Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6464304Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6464641Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6464955Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6465253Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6465593Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6465943Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6466266Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6466585Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6466873Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6467241Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6467589Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6467912Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6468228Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6468514Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6468859Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6469196Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6469533Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6469853Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6470174Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6470519Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6470869Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6471203Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6471514Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6471808Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6472147Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6472491Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6472818Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6473129Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6473420Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6473771Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6474115Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6474430Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6474748Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6475028Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6475371Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6475734Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6476058Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6476395Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6476679Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6477040Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6477374Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6477699Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6478010Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6478310Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6478647Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6478981Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6479302Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6479612Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6479914Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6480259Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6480599Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6480920Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6481240Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6481526Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6481868Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6482221Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6482541Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6482875Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6483160Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6483724Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6484062Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6484394Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6484712Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6485006Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6485360Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6485699Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6486032Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6486344Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6486657Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6487003Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6487350Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6487675Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6487984Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6488278Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6488637Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6488980Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6489322Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6489639Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6489955Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6490307Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6490650Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6490974Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6491310Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6491667Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6492039Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6492404Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6492772Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6493152Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6493481Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6493862Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6494201Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6494532Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6494845Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6495138Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6495502Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6495856Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6496196Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6496517Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6496827Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6497223Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6497597Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6497930Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6498275Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6498581Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6498931Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6499296Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6499642Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6500009Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6500315Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6500708Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6501067Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6501394Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6501713Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6502020Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6502362Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6502733Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6503087Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6503441Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6503757Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6504131Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6504496Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6504834Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6505180Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6505467Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6505818Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6506164Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6506496Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6506814Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6507095Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6507434Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6507767Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6508094Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6508403Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6508734Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6509107Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6509449Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6509779Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6510105Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6510398Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6510742Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6511086Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6511409Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6511730Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6512024Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6512366Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6512722Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6513050Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6513372Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6513657Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6514005Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6514337Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6514670Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6515007Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6515295Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6515657Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6515996Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6516352Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6516687Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6517000Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6517361Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6517727Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6518061Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6518391Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6518702Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6519063Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6519442Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6519789Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6520128Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6520433Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6520804Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6521174Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6521532Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6521870Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6522192Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6522565Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6522936Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6523284Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6523613Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6523926Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6524293Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6524648Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6524991Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6525319Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6525631Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6526006Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6526370Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6526690Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6527013Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6527302Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6527644Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6528006Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6528330Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6528666Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6528950Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6529313Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6529663Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6530019Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6530356Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6530656Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6531023Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6531379Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6531789Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6532135Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6532517Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6532912Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6533260Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6533589Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6533902Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6534201Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6534545Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6534905Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6535236Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6535552Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6535835Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6536199Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6536541Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6536867Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6537183Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6537474Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6537823Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6538158Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6538486Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6538799Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6539119Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6539472Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6539803Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6540130Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6540439Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6540738Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6541095Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6541441Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6541778Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6542097Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6542404Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6542746Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6543090Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6543412Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6543731Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6544020Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6544370Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6544710Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6545038Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6545375Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6545665Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6546021Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6546356Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6546684Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6546994Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6547306Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6547640Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6547994Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6548313Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6548672Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6548960Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6549294Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6549636Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6549960Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6550272Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6550552Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6550904Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6551249Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6551584Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6551910Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6552197Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6552549Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6552894Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6553214Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6553532Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6553821Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6554177Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6554512Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6554858Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6555171Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6555463Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6555805Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6556151Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6556482Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6556793Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6557078Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6557412Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6557757Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6558077Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6558387Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6558665Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6559006Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6559333Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6559651Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6559976Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6560259Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6560753Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6561094Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6561471Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6561789Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6562091Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6562434Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6562783Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6563117Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6563432Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6563729Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6564071Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6564454Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6564782Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6565105Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6565402Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6565745Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6566093Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6566441Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6566762Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6567075Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6567429Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6567792Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6568124Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6568440Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6568729Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6569080Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6569420Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6569757Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6570075Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6570376Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6570734Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6571088Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6571518Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6571933Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6572263Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6572720Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6573125Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6573485Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6573830Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6574117Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6574483Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6574833Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6575157Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6575478Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6575774Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6576162Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6576532Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6576869Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6577182Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6577500Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6577863Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6578208Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6578533Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6578843Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6579148Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:04.6579498Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6579877Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6580239Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:04.6580582Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:04.6580876Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6581237Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6581579Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6581901Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6582215Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6582502Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6582848Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6583180Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6583506Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6583819Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6584119Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6584470Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6584809Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6585139Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6585457Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6585750Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:04.6586104Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6586446Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6586796Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:04.6587112Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:04.6587421Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:04.6587785Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6588132Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6588450Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:04.6588769Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:04.6589051Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:04.6589397Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:04.6589738Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:04.6590067Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:04.6590390Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:04.6590741Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:53:04.6591064Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:53:04.6591373Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:53:04.6591746Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:53:04.6592110Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:53:04.6592483Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:53:04.6592838Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:53:04.6592915Z         
2025-03-14T04:53:04.6593193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6593659Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6593743Z         
2025-03-14T04:53:04.6594019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6595494Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6595565Z         
2025-03-14T04:53:04.6595851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:53:04.6596003Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:53:04.6596064Z         
2025-03-14T04:53:04.6596428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:53:04.6596663Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:53:04.6596732Z         
2025-03-14T04:53:04.6596996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6597430Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6597491Z         
2025-03-14T04:53:04.6597758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6599279Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6599353Z         
2025-03-14T04:53:04.6599653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6599784Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:53:04.6599851Z         
2025-03-14T04:53:04.6600094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6600537Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6600599Z         
2025-03-14T04:53:04.6600870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6602383Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6602455Z         
2025-03-14T04:53:04.6602754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6602893Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:53:04.6602963Z         
2025-03-14T04:53:04.6603214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6603680Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6603744Z         
2025-03-14T04:53:04.6604014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6605564Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6605645Z         
2025-03-14T04:53:04.6605902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6606350Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:04.6606436Z         
2025-03-14T04:53:04.6606702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6608326Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6608399Z         
2025-03-14T04:53:04.6608683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6608837Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:53:04.6608899Z         
2025-03-14T04:53:04.6609188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6609336Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:53:04.6609406Z         
2025-03-14T04:53:04.6609655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6610103Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6610167Z         
2025-03-14T04:53:04.6610438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6612148Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6612224Z         
2025-03-14T04:53:04.6612577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6612736Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:53:04.6612836Z         
2025-03-14T04:53:04.6613087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6613523Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6613602Z         
2025-03-14T04:53:04.6613873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6615405Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6615470Z         
2025-03-14T04:53:04.6615762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6615902Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:53:04.6615975Z         
2025-03-14T04:53:04.6616225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6616670Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6616735Z         
2025-03-14T04:53:04.6617024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6618568Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6618631Z         
2025-03-14T04:53:04.6618920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6619085Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:53:04.6619155Z         
2025-03-14T04:53:04.6619435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6619600Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:53:04.6619664Z         
2025-03-14T04:53:04.6619918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6620339Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6620423Z         
2025-03-14T04:53:04.6620697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6622231Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6622303Z         
2025-03-14T04:53:04.6622587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6622731Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:53:04.6622792Z         
2025-03-14T04:53:04.6623048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6623488Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6623558Z         
2025-03-14T04:53:04.6623825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6625360Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6625432Z         
2025-03-14T04:53:04.6625729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6625874Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:53:04.6625949Z         
2025-03-14T04:53:04.6626203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6626640Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6626715Z         
2025-03-14T04:53:04.6626988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6628540Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6628610Z         
2025-03-14T04:53:04.6628891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6629052Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:53:04.6629114Z         
2025-03-14T04:53:04.6629408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6629568Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:53:04.6629628Z         
2025-03-14T04:53:04.6629885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6630332Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6630403Z         
2025-03-14T04:53:04.6630694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6632278Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6632351Z         
2025-03-14T04:53:04.6632641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6632808Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:53:04.6632869Z         
2025-03-14T04:53:04.6633122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6633556Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6633666Z         
2025-03-14T04:53:04.6633933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6635496Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6635568Z         
2025-03-14T04:53:04.6635857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6636007Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:53:04.6636069Z         
2025-03-14T04:53:04.6636325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6636781Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6636852Z         
2025-03-14T04:53:04.6637122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6638693Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6638767Z         
2025-03-14T04:53:04.6639037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6639497Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:04.6639569Z         
2025-03-14T04:53:04.6639831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6641406Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6641474Z         
2025-03-14T04:53:04.6641756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6641901Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:53:04.6641967Z         
2025-03-14T04:53:04.6642240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6642393Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:53:04.6642453Z         
2025-03-14T04:53:04.6642698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6643127Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6643197Z         
2025-03-14T04:53:04.6643450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6644992Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6645061Z         
2025-03-14T04:53:04.6645368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6645519Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:53:04.6645579Z         
2025-03-14T04:53:04.6645836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6646283Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6646366Z         
2025-03-14T04:53:04.6646628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6648201Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6648272Z         
2025-03-14T04:53:04.6648554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6648703Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:53:04.6648763Z         
2025-03-14T04:53:04.6649021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6649457Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6649527Z         
2025-03-14T04:53:04.6649804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6651395Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6651531Z         
2025-03-14T04:53:04.6651831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6652021Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:53:04.6652087Z         
2025-03-14T04:53:04.6652390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6652562Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:53:04.6652634Z         
2025-03-14T04:53:04.6652901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6653333Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6653417Z         
2025-03-14T04:53:04.6653690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6655239Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6655309Z         
2025-03-14T04:53:04.6655606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6655757Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:53:04.6655829Z         
2025-03-14T04:53:04.6656072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6656541Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6656604Z         
2025-03-14T04:53:04.6656879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6658491Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6658557Z         
2025-03-14T04:53:04.6658859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6658998Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:53:04.6659083Z         
2025-03-14T04:53:04.6659331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6659776Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6659863Z         
2025-03-14T04:53:04.6660138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6661802Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6661871Z         
2025-03-14T04:53:04.6662168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6662322Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:53:04.6662397Z         
2025-03-14T04:53:04.6662681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6662838Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:53:04.6662900Z         
2025-03-14T04:53:04.6663157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6663627Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6663697Z         
2025-03-14T04:53:04.6663964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6665522Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6665596Z         
2025-03-14T04:53:04.6665885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6666052Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:53:04.6666112Z         
2025-03-14T04:53:04.6666373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6666814Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6666897Z         
2025-03-14T04:53:04.6667174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6668728Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6668803Z         
2025-03-14T04:53:04.6669089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6669246Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:53:04.6669306Z         
2025-03-14T04:53:04.6669557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6670005Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6670069Z         
2025-03-14T04:53:04.6670333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6671830Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6671899Z         
2025-03-14T04:53:04.6672183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6672339Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:53:04.6672419Z         
2025-03-14T04:53:04.6672691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6672838Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:53:04.6672898Z         
2025-03-14T04:53:04.6673147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6673573Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6673642Z         
2025-03-14T04:53:04.6673897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6675397Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6675467Z         
2025-03-14T04:53:04.6675744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6675881Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:53:04.6675941Z         
2025-03-14T04:53:04.6676202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6676621Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6676691Z         
2025-03-14T04:53:04.6676946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6678456Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6678526Z         
2025-03-14T04:53:04.6678798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6678951Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:53:04.6679010Z         
2025-03-14T04:53:04.6679256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6679667Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6679748Z         
2025-03-14T04:53:04.6680008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6681500Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6681574Z         
2025-03-14T04:53:04.6681816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6682248Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:04.6682313Z         
2025-03-14T04:53:04.6682595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6684208Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6684279Z         
2025-03-14T04:53:04.6684569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6684706Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:53:04.6684774Z         
2025-03-14T04:53:04.6685065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6685211Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:53:04.6685298Z         
2025-03-14T04:53:04.6685555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6685969Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6686055Z         
2025-03-14T04:53:04.6686316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6687853Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6687924Z         
2025-03-14T04:53:04.6688208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6688346Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:53:04.6688408Z         
2025-03-14T04:53:04.6688661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6689096Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6689169Z         
2025-03-14T04:53:04.6689430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6690966Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6691040Z         
2025-03-14T04:53:04.6691345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6691555Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:53:04.6691627Z         
2025-03-14T04:53:04.6691907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6692424Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6692497Z         
2025-03-14T04:53:04.6692798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6694355Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6694429Z         
2025-03-14T04:53:04.6694706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6694865Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:53:04.6694927Z         
2025-03-14T04:53:04.6695213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6695354Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:53:04.6695425Z         
2025-03-14T04:53:04.6695672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6696111Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6696175Z         
2025-03-14T04:53:04.6696445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6697960Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6698023Z         
2025-03-14T04:53:04.6698354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6698484Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:53:04.6698571Z         
2025-03-14T04:53:04.6698818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6699253Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6699328Z         
2025-03-14T04:53:04.6699599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6701156Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6701220Z         
2025-03-14T04:53:04.6701510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6701643Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:53:04.6701712Z         
2025-03-14T04:53:04.6701960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6702407Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6702469Z         
2025-03-14T04:53:04.6702745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6704317Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6704382Z         
2025-03-14T04:53:04.6704664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6704824Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:53:04.6704895Z         
2025-03-14T04:53:04.6705177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6705340Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:53:04.6705400Z         
2025-03-14T04:53:04.6705651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6706084Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6706153Z         
2025-03-14T04:53:04.6706416Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6707957Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6708031Z         
2025-03-14T04:53:04.6708317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6708457Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:53:04.6708517Z         
2025-03-14T04:53:04.6708775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6709214Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6709285Z         
2025-03-14T04:53:04.6709556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6711080Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6711152Z         
2025-03-14T04:53:04.6711458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6711596Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:53:04.6711671Z         
2025-03-14T04:53:04.6711934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6712369Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6712447Z         
2025-03-14T04:53:04.6712721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6714266Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6714340Z         
2025-03-14T04:53:04.6714617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6714770Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:53:04.6714833Z         
2025-03-14T04:53:04.6715123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6715262Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:53:04.6715332Z         
2025-03-14T04:53:04.6715596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6716019Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6716086Z         
2025-03-14T04:53:04.6716348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6717930Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6717992Z         
2025-03-14T04:53:04.6718282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6718437Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:53:04.6718498Z         
2025-03-14T04:53:04.6718749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6719186Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6719254Z         
2025-03-14T04:53:04.6719524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6721151Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6721225Z         
2025-03-14T04:53:04.6721518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6721663Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:53:04.6721725Z         
2025-03-14T04:53:04.6721992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6722460Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6722540Z         
2025-03-14T04:53:04.6722830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6724472Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6724550Z         
2025-03-14T04:53:04.6724861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6725019Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:53:04.6725101Z         
2025-03-14T04:53:04.6725410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6725555Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:53:04.6725626Z         
2025-03-14T04:53:04.6725902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6726350Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6726415Z         
2025-03-14T04:53:04.6726698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6728306Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6728376Z         
2025-03-14T04:53:04.6728666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6728797Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:53:04.6728865Z         
2025-03-14T04:53:04.6729125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6729563Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6729623Z         
2025-03-14T04:53:04.6729893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6731505Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6731583Z         
2025-03-14T04:53:04.6731877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6732037Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:53:04.6732113Z         
2025-03-14T04:53:04.6732387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6732876Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6732940Z         
2025-03-14T04:53:04.6733225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6734798Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6734871Z         
2025-03-14T04:53:04.6735151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6735295Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:53:04.6735362Z         
2025-03-14T04:53:04.6735640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6735807Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:53:04.6735870Z         
2025-03-14T04:53:04.6736125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6736537Z         x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6736607Z         
2025-03-14T04:53:04.6736872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6738409Z         x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6738785Z         
2025-03-14T04:53:04.6739071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6739211Z         out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:53:04.6739274Z         
2025-03-14T04:53:04.6739543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6739964Z         x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6740038Z         
2025-03-14T04:53:04.6740299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6741862Z         x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6741934Z         
2025-03-14T04:53:04.6742215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6742353Z         out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:53:04.6742415Z         
2025-03-14T04:53:04.6742684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6743114Z         x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6743187Z         
2025-03-14T04:53:04.6743456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6745026Z         x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6745099Z         
2025-03-14T04:53:04.6745374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6745543Z         x_93 += out_51;  out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:53:04.6745605Z         
2025-03-14T04:53:04.6745891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6746043Z         out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:53:04.6746112Z         
2025-03-14T04:53:04.6746358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6746782Z         x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6746845Z         
2025-03-14T04:53:04.6747113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6748640Z         x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6748704Z         
2025-03-14T04:53:04.6748983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6749122Z         out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:53:04.6749191Z         
2025-03-14T04:53:04.6749434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6749851Z         x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6749911Z         
2025-03-14T04:53:04.6750179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6751683Z         x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6751760Z         
2025-03-14T04:53:04.6752041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6752168Z         out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:53:04.6752235Z         
2025-03-14T04:53:04.6752490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6752912Z         x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6752971Z         
2025-03-14T04:53:04.6753234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6754723Z         x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6754785Z         
2025-03-14T04:53:04.6755059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6755197Z         x_99 += out_55;  out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:53:04.6755263Z         
2025-03-14T04:53:04.6755551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6755694Z         out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:53:04.6755751Z         
2025-03-14T04:53:04.6756000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6756405Z         x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6756471Z         
2025-03-14T04:53:04.6756733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6758236Z         x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6758319Z         
2025-03-14T04:53:04.6758596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6758766Z         out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:53:04.6758825Z         
2025-03-14T04:53:04.6759072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6759487Z         x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6759548Z         
2025-03-14T04:53:04.6759807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6761379Z         x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6761455Z         
2025-03-14T04:53:04.6761733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6761903Z         out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:53:04.6761964Z         
2025-03-14T04:53:04.6762212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6762632Z         x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6762694Z         
2025-03-14T04:53:04.6762956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6764451Z         x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6764541Z         
2025-03-14T04:53:04.6764814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6764978Z         x_105 += out_59;  out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:53:04.6765066Z         
2025-03-14T04:53:04.6765342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6765485Z         out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:53:04.6765547Z         
2025-03-14T04:53:04.6765796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6766201Z         x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6766269Z         
2025-03-14T04:53:04.6766525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6768033Z         x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6768103Z         
2025-03-14T04:53:04.6768395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6768538Z         out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:53:04.6768599Z         
2025-03-14T04:53:04.6768852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6769261Z         x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6769331Z         
2025-03-14T04:53:04.6769591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6771132Z         x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6771217Z         
2025-03-14T04:53:04.6771550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6771734Z         out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:53:04.6771800Z         
2025-03-14T04:53:04.6772086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6772548Z         x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6772628Z         
2025-03-14T04:53:04.6772919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6774469Z         x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6774547Z         
2025-03-14T04:53:04.6774848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6775025Z         x_111 += out_63;  out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:53:04.6775088Z         
2025-03-14T04:53:04.6775397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6775534Z         out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:53:04.6775602Z         
2025-03-14T04:53:04.6775856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6776324Z         x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6776384Z         
2025-03-14T04:53:04.6776665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6778273Z         x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6778372Z         
2025-03-14T04:53:04.6778673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6778806Z         out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:53:04.6778877Z         
2025-03-14T04:53:04.6779133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6779604Z         x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6779666Z         
2025-03-14T04:53:04.6779946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6781500Z         x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6781565Z         
2025-03-14T04:53:04.6781890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6782027Z         out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:53:04.6782095Z         
2025-03-14T04:53:04.6782355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6782832Z         x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6782895Z         
2025-03-14T04:53:04.6783173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6784775Z         x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6784859Z         
2025-03-14T04:53:04.6785143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6785306Z         x_117 += out_67;  out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:53:04.6785375Z         
2025-03-14T04:53:04.6785652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6785799Z         out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:53:04.6785862Z         
2025-03-14T04:53:04.6786125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6786562Z         x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6786634Z         
2025-03-14T04:53:04.6786906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6788543Z         x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6788617Z         
2025-03-14T04:53:04.6788896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6789036Z         out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:53:04.6789096Z         
2025-03-14T04:53:04.6789342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6789761Z         x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6789832Z         
2025-03-14T04:53:04.6790093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6791654Z         x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6791749Z         
2025-03-14T04:53:04.6792024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6792161Z         out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:53:04.6792221Z         
2025-03-14T04:53:04.6792467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6792886Z         x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6792956Z         
2025-03-14T04:53:04.6793209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6794715Z         x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6794802Z         
2025-03-14T04:53:04.6795079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6795231Z         x_123 += out_71;  out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:53:04.6795290Z         
2025-03-14T04:53:04.6795576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6795708Z         out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:53:04.6795776Z         
2025-03-14T04:53:04.6796018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6796434Z         x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6796503Z         
2025-03-14T04:53:04.6796782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6798299Z         x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6798388Z         
2025-03-14T04:53:04.6798676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6798805Z         out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:53:04.6798873Z         
2025-03-14T04:53:04.6799115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6799539Z         x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6799607Z         
2025-03-14T04:53:04.6799862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6801386Z         x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6801449Z         
2025-03-14T04:53:04.6801733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6801869Z         out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:53:04.6801928Z         
2025-03-14T04:53:04.6802178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6802593Z         x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6802663Z         
2025-03-14T04:53:04.6802922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6804450Z         x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6804544Z         
2025-03-14T04:53:04.6804816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6804969Z         x_129 += out_75;  out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:53:04.6805029Z         
2025-03-14T04:53:04.6805313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6805450Z         out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:53:04.6805519Z         
2025-03-14T04:53:04.6805768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6806206Z         x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6806269Z         
2025-03-14T04:53:04.6806532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6808049Z         x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6808119Z         
2025-03-14T04:53:04.6808400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6808530Z         out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:53:04.6808596Z         
2025-03-14T04:53:04.6808833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6809263Z         x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6809323Z         
2025-03-14T04:53:04.6809610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6811157Z         x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6811255Z         
2025-03-14T04:53:04.6811599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6811739Z         out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:53:04.6811805Z         
2025-03-14T04:53:04.6812051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6812497Z         x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6812561Z         
2025-03-14T04:53:04.6812856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6814511Z         x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6814584Z         
2025-03-14T04:53:04.6814866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6815014Z         x_135 += out_79;  out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:53:04.6815085Z         
2025-03-14T04:53:04.6815362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6815505Z         out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:53:04.6815564Z         
2025-03-14T04:53:04.6815818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6816246Z         x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6816314Z         
2025-03-14T04:53:04.6816576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6818166Z         x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6818253Z         
2025-03-14T04:53:04.6818533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6818675Z         out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:53:04.6818735Z         
2025-03-14T04:53:04.6818988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6819409Z         x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6819480Z         
2025-03-14T04:53:04.6819739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6821333Z         x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6821403Z         
2025-03-14T04:53:04.6821691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6821834Z         out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:53:04.6821896Z         
2025-03-14T04:53:04.6822154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6822587Z         x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6822673Z         
2025-03-14T04:53:04.6822935Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6824518Z         x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6824617Z         
2025-03-14T04:53:04.6824901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6825062Z         x_141 += out_83;  out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:53:04.6825123Z         
2025-03-14T04:53:04.6825414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6825553Z         out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:53:04.6825627Z         
2025-03-14T04:53:04.6825873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6826307Z         x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6826369Z         
2025-03-14T04:53:04.6826632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6828165Z         x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6828258Z         
2025-03-14T04:53:04.6828539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6828666Z         out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:53:04.6828734Z         
2025-03-14T04:53:04.6828973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6829402Z         x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6829463Z         
2025-03-14T04:53:04.6829739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6831234Z         x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6831309Z         
2025-03-14T04:53:04.6831594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6831726Z         out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:53:04.6831798Z         
2025-03-14T04:53:04.6832040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6832463Z         x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6832530Z         
2025-03-14T04:53:04.6832784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6834320Z         x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6834384Z         
2025-03-14T04:53:04.6834664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6834810Z         x_147 += out_87;  out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:53:04.6834879Z         
2025-03-14T04:53:04.6835155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6835296Z         out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:53:04.6835355Z         
2025-03-14T04:53:04.6835617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6836031Z         x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6836104Z         
2025-03-14T04:53:04.6836375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6837860Z         x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6837944Z         
2025-03-14T04:53:04.6838221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6838358Z         out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:53:04.6838425Z         
2025-03-14T04:53:04.6838665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6839086Z         x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6839147Z         
2025-03-14T04:53:04.6839404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6840931Z         x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6841003Z         
2025-03-14T04:53:04.6841284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6841412Z         out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:53:04.6841480Z         
2025-03-14T04:53:04.6841717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6842209Z         x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6842284Z         
2025-03-14T04:53:04.6842553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6844054Z         x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6844137Z         
2025-03-14T04:53:04.6844417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6844563Z         x_153 += out_91;  out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:53:04.6844636Z         
2025-03-14T04:53:04.6844912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6845056Z         out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:53:04.6845121Z         
2025-03-14T04:53:04.6845377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6845802Z         x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6845873Z         
2025-03-14T04:53:04.6846147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6847699Z         x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6847771Z         
2025-03-14T04:53:04.6848054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6848194Z         out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:53:04.6848255Z         
2025-03-14T04:53:04.6848521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6848954Z         x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6849041Z         
2025-03-14T04:53:04.6849304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6850909Z         x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6850983Z         
2025-03-14T04:53:04.6851283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6851480Z         out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:53:04.6851554Z         
2025-03-14T04:53:04.6851839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6852341Z         x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6852425Z         
2025-03-14T04:53:04.6852740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6854377Z         x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6854451Z         
2025-03-14T04:53:04.6854728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6854888Z         x_159 += out_95;  out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:53:04.6854951Z         
2025-03-14T04:53:04.6855254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6855394Z         out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:53:04.6855464Z         
2025-03-14T04:53:04.6855723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6856176Z         x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6856235Z         
2025-03-14T04:53:04.6856524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6858098Z         x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6858162Z         
2025-03-14T04:53:04.6858448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6858587Z         out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:53:04.6858656Z         
2025-03-14T04:53:04.6858902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6859342Z         x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6859404Z         
2025-03-14T04:53:04.6859699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6861365Z         x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6861435Z         
2025-03-14T04:53:04.6861728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6861864Z         out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:53:04.6861966Z         
2025-03-14T04:53:04.6862213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6862684Z         x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6862749Z         
2025-03-14T04:53:04.6863020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6864631Z         x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6864705Z         
2025-03-14T04:53:04.6864982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6865130Z         x_165 += out_99;  out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:53:04.6865199Z         
2025-03-14T04:53:04.6865470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6865618Z         out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:53:04.6865678Z         
2025-03-14T04:53:04.6865924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6866347Z         x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6866414Z         
2025-03-14T04:53:04.6866670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6868178Z         x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6868249Z         
2025-03-14T04:53:04.6868536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6868676Z         out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:53:04.6868751Z         
2025-03-14T04:53:04.6868996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6869409Z         x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6869492Z         
2025-03-14T04:53:04.6869758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6871269Z         x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6871338Z         
2025-03-14T04:53:04.6871614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6871757Z         out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:53:04.6871816Z         
2025-03-14T04:53:04.6872066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6872495Z         x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6872556Z         
2025-03-14T04:53:04.6872835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6874359Z         x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6874430Z         
2025-03-14T04:53:04.6874701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6874878Z         x_171 += out_103;  out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:53:04.6874946Z         
2025-03-14T04:53:04.6875222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6875383Z         out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:53:04.6875445Z         
2025-03-14T04:53:04.6875693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6876101Z         x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6876182Z         
2025-03-14T04:53:04.6876437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6877954Z         x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6878023Z         
2025-03-14T04:53:04.6878295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6878435Z         out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:53:04.6878494Z         
2025-03-14T04:53:04.6878740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6879167Z         x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6879239Z         
2025-03-14T04:53:04.6879497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6881026Z         x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6881106Z         
2025-03-14T04:53:04.6881398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6881539Z         out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:53:04.6881624Z         
2025-03-14T04:53:04.6881882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6882311Z         x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6882394Z         
2025-03-14T04:53:04.6882656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6884214Z         x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6884287Z         
2025-03-14T04:53:04.6884562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6884726Z         x_177 += out_107;  out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:53:04.6884790Z         
2025-03-14T04:53:04.6885079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6885221Z         out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:53:04.6885291Z         
2025-03-14T04:53:04.6885552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6885981Z         x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6886043Z         
2025-03-14T04:53:04.6886314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6887885Z         x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6887956Z         
2025-03-14T04:53:04.6888262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6888395Z         out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:53:04.6888463Z         
2025-03-14T04:53:04.6888707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6889162Z         x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6889228Z         
2025-03-14T04:53:04.6889497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6891063Z         x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6891128Z         
2025-03-14T04:53:04.6891417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6891600Z         out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:53:04.6891674Z         
2025-03-14T04:53:04.6891948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6892437Z         x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6892505Z         
2025-03-14T04:53:04.6892799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6894432Z         x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6894495Z         
2025-03-14T04:53:04.6894784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6894961Z         x_183 += out_111;  out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:53:04.6895029Z         
2025-03-14T04:53:04.6895308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6895460Z         out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:53:04.6895540Z         
2025-03-14T04:53:04.6895812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6896246Z         x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6896318Z         
2025-03-14T04:53:04.6896593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6898198Z         x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6898269Z         
2025-03-14T04:53:04.6898558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6898699Z         out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:53:04.6898760Z         
2025-03-14T04:53:04.6899030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6899458Z         x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6899528Z         
2025-03-14T04:53:04.6899790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6901369Z         x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6901455Z         
2025-03-14T04:53:04.6901738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6901877Z         out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:53:04.6901939Z         
2025-03-14T04:53:04.6902192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6902634Z         x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6902705Z         
2025-03-14T04:53:04.6902966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6904518Z         x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6904590Z         
2025-03-14T04:53:04.6904867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6905027Z         x_189 += out_115;  out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:53:04.6905089Z         
2025-03-14T04:53:04.6905390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6905537Z         out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:53:04.6905608Z         
2025-03-14T04:53:04.6905856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6906281Z         x_190: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6906353Z         
2025-03-14T04:53:04.6906613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6908187Z         x_191: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_190 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6908263Z         
2025-03-14T04:53:04.6908546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6908697Z         out_120: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_191);  x_191 = None
2025-03-14T04:53:04.6908762Z         
2025-03-14T04:53:04.6909006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6909429Z         x_192: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_120 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6909497Z         
2025-03-14T04:53:04.6909751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6911250Z         x_193: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_192 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6911313Z         
2025-03-14T04:53:04.6911596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6911732Z         out_121: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_193);  x_193 = None
2025-03-14T04:53:04.6911805Z         
2025-03-14T04:53:04.6912053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6912475Z         x_194: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_121 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6912542Z         
2025-03-14T04:53:04.6912797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6914322Z         x_195: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_194 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6914407Z         
2025-03-14T04:53:04.6914651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6915086Z         x_196: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_119 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:04.6915160Z         
2025-03-14T04:53:04.6915423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6916976Z         x_197: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_196 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6917051Z         
2025-03-14T04:53:04.6917333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6917483Z         x_195 += x_197;  out_122: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_195;  x_195 = x_197 = None
2025-03-14T04:53:04.6917554Z         
2025-03-14T04:53:04.6917829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6917979Z         out_123: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_122);  out_122 = None
2025-03-14T04:53:04.6918043Z         
2025-03-14T04:53:04.6918308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6918723Z         x_198: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6918792Z         
2025-03-14T04:53:04.6919050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6920553Z         x_199: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_198 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6920624Z         
2025-03-14T04:53:04.6920915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6921052Z         out_124: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_199);  x_199 = None
2025-03-14T04:53:04.6921110Z         
2025-03-14T04:53:04.6921359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6921803Z         x_200: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_124 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6921871Z         
2025-03-14T04:53:04.6922135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6923659Z         x_201: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_200 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6923728Z         
2025-03-14T04:53:04.6924011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6924153Z         out_125: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_201);  x_201 = None
2025-03-14T04:53:04.6924213Z         
2025-03-14T04:53:04.6924466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6924903Z         x_202: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_125 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6924972Z         
2025-03-14T04:53:04.6925237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6926796Z         x_203: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_202 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6926868Z         
2025-03-14T04:53:04.6927148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6927329Z         x_203 += out_123;  out_126: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_203;  x_203 = out_123 = None
2025-03-14T04:53:04.6927390Z         
2025-03-14T04:53:04.6927678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6927823Z         out_127: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_126);  out_126 = None
2025-03-14T04:53:04.6927908Z         
2025-03-14T04:53:04.6928154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6928573Z         x_204: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:04.6928638Z         
2025-03-14T04:53:04.6928907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6930438Z         x_205: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_204 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6930501Z         
2025-03-14T04:53:04.6930786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6930919Z         out_128: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_205);  x_205 = None
2025-03-14T04:53:04.6930985Z         
2025-03-14T04:53:04.6931251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6931777Z         x_206: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_128 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:04.6931850Z         
2025-03-14T04:53:04.6932147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6933755Z         x_207: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_206 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6933834Z         
2025-03-14T04:53:04.6934125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6934261Z         out_129: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_207);  x_207 = None
2025-03-14T04:53:04.6934335Z         
2025-03-14T04:53:04.6934595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6935060Z         x_208: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_129 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:04.6935122Z         
2025-03-14T04:53:04.6935392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:04.6936961Z         x_209: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_208 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:04.6937025Z         
2025-03-14T04:53:04.6937308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:04.6937466Z         x_209 += out_127;  out_130: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_209;  x_209 = out_127 = None
2025-03-14T04:53:04.6937536Z         
2025-03-14T04:53:04.6937826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:04.6937981Z         out_131: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_130);  out_130 = None
2025-03-14T04:53:04.6938041Z         
2025-03-14T04:53:04.6938484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:04.6938633Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:53:04.6938704Z         
2025-03-14T04:53:04.6938997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:04.6939140Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:53:04.6939202Z         
2025-03-14T04:53:04.6939644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:04.6939795Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:53:04.6939880Z         
2025-03-14T04:53:04.6940175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:04.6940335Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:53:04.6940403Z         
2025-03-14T04:53:04.6940779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:53:04.6940958Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:53:04.6941067Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:53:04.6941188Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:53:04.6941249Z         
2025-03-14T04:53:04.6941582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:53:04.6941703Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:53:04.6941772Z         
2025-03-14T04:53:04.6942086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:53:04.6942208Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:53:04.6942267Z         
2025-03-14T04:53:04.6942643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:53:04.6942853Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:53:04.6942919Z         
2025-03-14T04:53:04.6943323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:53:04.6943453Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:53:04.6943866Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:53:04.6944040Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:53:04.6944153Z         x_210: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:53:04.6944219Z         
2025-03-14T04:53:04.6944509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:04.6944636Z         tensor: "f32[82125, 4][4, 1]cpu" = x_210.to(torch.float32);  x_210 = None
2025-03-14T04:53:04.6944695Z         
2025-03-14T04:53:04.6944945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:04.6945703Z         x_211: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_131, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_131 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:53:04.6945773Z         
2025-03-14T04:53:04.6946058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:53:04.6946246Z         x_212: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_211, inplace = False);  x_211 = None
2025-03-14T04:53:04.6946329Z         
2025-03-14T04:53:04.6946698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:53:04.6947537Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:53:04.6947613Z         
2025-03-14T04:53:04.6947966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:53:04.6948768Z         x_213: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_212 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:53:04.6948836Z         
2025-03-14T04:53:04.6949173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:53:04.6949324Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:53:04.6949463Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:53:04.6949525Z         
2025-03-14T04:53:04.6949938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:53:04.6950089Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_213.view(4, -1, 4, 73, 75);  x_213 = None
2025-03-14T04:53:04.6950275Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:53:04.6950449Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:53:04.6950516Z         
2025-03-14T04:53:04.6950899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:53:04.6951103Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:53:04.6951164Z         
2025-03-14T04:53:04.6951588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:53:04.6951729Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:53:04.6951877Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:53:04.6952010Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:53:04.6952076Z         
2025-03-14T04:53:04.6952452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:04.6952641Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:53:04.6952702Z         
2025-03-14T04:53:04.6953010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:04.6953145Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:53:04.6953234Z         
2025-03-14T04:53:04.6953535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:04.6953668Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:04.6953789Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:04.6953937Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:53:04.6953999Z         
2025-03-14T04:53:04.6954315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:04.6954437Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:04.6954549Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:04.6954699Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:04.6954758Z         
2025-03-14T04:53:04.6955064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:04.6955177Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:04.6955268Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:53:04.6955386Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:53:04.6955452Z         
2025-03-14T04:53:04.6955750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:04.6955892Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:04.6955993Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:53:04.6956123Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:53:04.6956184Z         
2025-03-14T04:53:04.6956506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:04.6956651Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:04.6956771Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:53:04.6956831Z         
2025-03-14T04:53:04.6957133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:04.6957276Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:04.6957394Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:53:04.6957454Z         
2025-03-14T04:53:04.6957749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:04.6957909Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:04.6958023Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:53:04.6958097Z         
2025-03-14T04:53:04.6958400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:04.6958580Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:04.6958694Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:53:04.6958771Z         
2025-03-14T04:53:04.6959102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:04.6959238Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:04.6959306Z         
2025-03-14T04:53:04.6959620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:04.6959758Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:04.6959817Z         
2025-03-14T04:53:04.6960158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:04.6960295Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:04.6960420Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:53:04.6960683Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:04.6960834Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:53:04.6960892Z         
2025-03-14T04:53:04.6961237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:04.6961377Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:04.6961493Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:53:04.6961679Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:04.6961811Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:53:04.6961878Z         
2025-03-14T04:53:04.6962200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:04.6962317Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:04.6962469Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:04.6962602Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:53:04.6962664Z         
2025-03-14T04:53:04.6962993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:04.6963105Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:04.6963272Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:04.6963398Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:53:04.6963488Z         
2025-03-14T04:53:04.6963789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:04.6963906Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:04.6964016Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:04.6964085Z         
2025-03-14T04:53:04.6964380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:04.6964473Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:04.6964604Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:04.6964672Z         
2025-03-14T04:53:04.6964966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:04.6965084Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:04.6965205Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:04.6965274Z         
2025-03-14T04:53:04.6965565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:04.6965679Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:04.6965797Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:04.6965869Z         
2025-03-14T04:53:04.6966201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:04.6966380Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:04.6966442Z         
2025-03-14T04:53:04.6966772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:04.6966927Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:53:04.6966997Z         
2025-03-14T04:53:04.6967367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:53:04.6967558Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:53:04.6967619Z         
2025-03-14T04:53:04.6968096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:53:04.6968232Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:53:04.6968301Z         
2025-03-14T04:53:04.6968584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:04.6968721Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:53:04.6968781Z         
2025-03-14T04:53:04.6969206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:53:04.6969321Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:53:04.6969434Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:53:04.6969551Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:53:04.6969611Z         
2025-03-14T04:53:04.6970065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:53:04.6970235Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:53:04.6970469Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:53:04.6970542Z         
2025-03-14T04:53:04.6970995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:53:04.6971156Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:53:04.6971224Z         
2025-03-14T04:53:04.6971562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:04.6971725Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:53:04.6971785Z         
2025-03-14T04:53:04.6972198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:53:04.6972348Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:53:04.6972431Z         
2025-03-14T04:53:04.6972777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:04.6972960Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:53:04.6973041Z         
2025-03-14T04:53:04.6973481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:53:04.6973618Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:53:04.6973689Z         
2025-03-14T04:53:04.6974257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:53:04.6974410Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:53:04.6974544Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:04.6974709Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:53:04.6974837Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:53:04.6974908Z         
2025-03-14T04:53:04.6975298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:53:04.6975424Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:53:04.6975501Z         
2025-03-14T04:53:22.9848064Z 
2025-03-14T04:53:22.9851554Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:22.9853813Z     def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:53:22.9855507Z         l_features_res5_ = L_features_res5_
2025-03-14T04:53:22.9855933Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:53:22.9856529Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:53:22.9857030Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:53:22.9857624Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:53:22.9858285Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:53:22.9858871Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:53:22.9859445Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:53:22.9859824Z         
2025-03-14T04:53:22.9860431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:22.9861419Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:53:22.9861706Z         
2025-03-14T04:53:22.9862117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:22.9862671Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:53:22.9862941Z         
2025-03-14T04:53:22.9863555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:22.9864233Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:53:22.9864524Z         
2025-03-14T04:53:22.9864932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:22.9865446Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:53:22.9865720Z         
2025-03-14T04:53:22.9866212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:53:22.9866871Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:53:22.9867226Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:53:22.9867512Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:53:22.9867767Z         
2025-03-14T04:53:22.9868212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:53:22.9868796Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:53:22.9869051Z         
2025-03-14T04:53:22.9869455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:53:22.9869978Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:53:22.9870214Z         
2025-03-14T04:53:22.9870678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:53:22.9871339Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:53:22.9871656Z         
2025-03-14T04:53:22.9872150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:53:22.9872733Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:53:22.9873226Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:53:22.9873706Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:53:22.9873995Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:53:22.9874225Z         
2025-03-14T04:53:22.9874611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:22.9875135Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:53:22.9875372Z         
2025-03-14T04:53:22.9875704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:22.9876619Z         x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:53:22.9877321Z         
2025-03-14T04:53:22.9877706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:53:22.9878233Z         x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:53:22.9878538Z         
2025-03-14T04:53:22.9879010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:53:22.9880109Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:53:22.9880876Z         
2025-03-14T04:53:22.9881330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:53:22.9882386Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:53:22.9883127Z         
2025-03-14T04:53:22.9883562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:53:22.9884114Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:53:22.9884463Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:53:22.9884739Z         
2025-03-14T04:53:22.9885262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:53:22.9885888Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T04:53:22.9886262Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:53:22.9886656Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:53:22.9886947Z         
2025-03-14T04:53:22.9887430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:53:22.9888085Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:53:22.9888402Z         
2025-03-14T04:53:22.9888912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:53:22.9889539Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:53:22.9889888Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:53:22.9890228Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:53:22.9890481Z         
2025-03-14T04:53:22.9890954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:22.9891637Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:53:22.9891949Z         
2025-03-14T04:53:22.9892381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:22.9892937Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:53:22.9893211Z         
2025-03-14T04:53:22.9893626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:22.9894142Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:22.9894463Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:22.9894800Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:53:22.9895078Z         
2025-03-14T04:53:22.9895496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:22.9896708Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:22.9897041Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:22.9897411Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:22.9897699Z         
2025-03-14T04:53:22.9898126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:22.9898621Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:22.9898945Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:53:22.9899216Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:53:22.9899454Z         
2025-03-14T04:53:22.9899852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:22.9900360Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:22.9900653Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:53:22.9900924Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:53:22.9901166Z         
2025-03-14T04:53:22.9901698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:22.9902251Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:22.9902581Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:53:22.9902817Z         
2025-03-14T04:53:22.9903205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:22.9903709Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:22.9904033Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:53:22.9904264Z         
2025-03-14T04:53:22.9904651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:22.9905155Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:22.9905499Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:53:22.9905734Z         
2025-03-14T04:53:22.9906121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:22.9907136Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:22.9907486Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:53:22.9907715Z         
2025-03-14T04:53:22.9908131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:22.9908663Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:22.9908919Z         
2025-03-14T04:53:22.9909333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:22.9909852Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:22.9910102Z         
2025-03-14T04:53:22.9910550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:22.9911084Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:22.9911418Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:53:22.9911746Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:22.9912092Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:53:22.9912347Z         
2025-03-14T04:53:22.9912799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:22.9913340Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:22.9913654Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:53:22.9913983Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:22.9914324Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:53:22.9914580Z         
2025-03-14T04:53:22.9914991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:22.9915483Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:22.9915811Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:22.9916153Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:53:22.9916401Z         
2025-03-14T04:53:22.9916826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:22.9917342Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:22.9917673Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:22.9918021Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:53:22.9918272Z         
2025-03-14T04:53:22.9918682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:22.9919144Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:22.9919408Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:22.9919641Z         
2025-03-14T04:53:22.9920036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:22.9920506Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:22.9920772Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:22.9921009Z         
2025-03-14T04:53:22.9921408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:22.9921901Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:22.9922205Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:22.9922456Z         
2025-03-14T04:53:22.9922839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:22.9923335Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:22.9923622Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:22.9923869Z         
2025-03-14T04:53:22.9924341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:22.9924932Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:22.9925234Z         
2025-03-14T04:53:22.9925661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:22.9926239Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:53:22.9926525Z         
2025-03-14T04:53:22.9927002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:53:22.9927626Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:53:22.9927921Z         
2025-03-14T04:53:22.9928504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:53:22.9929200Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:53:22.9929457Z         
2025-03-14T04:53:22.9929842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:22.9930341Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:53:22.9930605Z         
2025-03-14T04:53:22.9931125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:53:22.9931822Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:53:22.9932115Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:53:22.9932412Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:53:22.9932671Z         
2025-03-14T04:53:22.9933255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:53:22.9933953Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:53:22.9934425Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:53:22.9934783Z         
2025-03-14T04:53:22.9935342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:53:22.9936035Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:53:22.9936323Z         
2025-03-14T04:53:22.9936717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:22.9937227Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:53:22.9937516Z         
2025-03-14T04:53:22.9937995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:53:22.9938620Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:53:22.9938884Z         
2025-03-14T04:53:22.9939260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:22.9939747Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:53:22.9940030Z         
2025-03-14T04:53:22.9940492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:53:22.9941057Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:53:22.9941319Z         
2025-03-14T04:53:22.9941886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:53:22.9942556Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:53:22.9942866Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:22.9943195Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:53:22.9943536Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:53:22.9943788Z         
2025-03-14T04:53:22.9944247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:53:22.9944787Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:53:22.9945026Z         
2025-03-14T04:53:22.9945168Z 
2025-03-14T04:53:22.9945265Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:22.9946628Z     def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:53:22.9947926Z         l_features_res5_ = L_features_res5_
2025-03-14T04:53:22.9948319Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:53:22.9948837Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:53:22.9949306Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:53:22.9949828Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:53:22.9950400Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:53:22.9950964Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:53:22.9951502Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:53:22.9951889Z         
2025-03-14T04:53:22.9952420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:22.9953070Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:53:22.9953335Z         
2025-03-14T04:53:22.9953725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:22.9954194Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:53:22.9954432Z         
2025-03-14T04:53:22.9954934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:22.9955547Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:53:22.9955804Z         
2025-03-14T04:53:22.9956168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:22.9956640Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:53:22.9956889Z         
2025-03-14T04:53:22.9957327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:53:22.9957909Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:53:22.9958232Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:53:22.9958492Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:53:22.9958723Z         
2025-03-14T04:53:22.9959146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:53:22.9959633Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:53:22.9959866Z         
2025-03-14T04:53:22.9960268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:53:22.9960924Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:53:22.9961164Z         
2025-03-14T04:53:22.9961643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:53:22.9962300Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:53:22.9962629Z         
2025-03-14T04:53:22.9963138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:53:22.9963717Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:53:22.9964203Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:53:22.9964726Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:53:22.9965009Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:53:22.9965240Z         
2025-03-14T04:53:22.9965657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:22.9966134Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:53:22.9966375Z         
2025-03-14T04:53:22.9966721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:22.9967655Z         x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:53:22.9968368Z         
2025-03-14T04:53:22.9968723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:53:22.9969233Z         x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:53:22.9969530Z         
2025-03-14T04:53:22.9969993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:53:22.9971058Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:53:22.9971897Z         
2025-03-14T04:53:22.9972354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:53:22.9973412Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:53:22.9974130Z         
2025-03-14T04:53:22.9974554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:53:22.9975096Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:53:22.9975439Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:53:22.9975694Z         
2025-03-14T04:53:22.9976194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:53:22.9976808Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T04:53:22.9977188Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:53:22.9977585Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:53:22.9977877Z         
2025-03-14T04:53:22.9978380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:53:22.9979057Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:53:22.9979370Z         
2025-03-14T04:53:22.9979888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:53:22.9980516Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:53:22.9980885Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:53:22.9981228Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:53:22.9981485Z         
2025-03-14T04:53:22.9981947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:22.9982545Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:53:22.9982838Z         
2025-03-14T04:53:22.9983239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:22.9983752Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:53:22.9984015Z         
2025-03-14T04:53:22.9984412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:22.9984912Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:22.9985224Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:22.9985553Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:53:22.9985823Z         
2025-03-14T04:53:22.9986225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:22.9986725Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:22.9987044Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:22.9987366Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:22.9987632Z         
2025-03-14T04:53:22.9988031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:22.9988516Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:22.9988782Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:53:22.9989047Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:53:22.9989287Z         
2025-03-14T04:53:22.9989679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:22.9990182Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:22.9990474Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:53:22.9990742Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:53:22.9990986Z         
2025-03-14T04:53:22.9991410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:22.9991918Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:22.9992259Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:53:22.9992486Z         
2025-03-14T04:53:22.9992868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:22.9993371Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:22.9993714Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:53:22.9993946Z         
2025-03-14T04:53:22.9994324Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:22.9994829Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:22.9995149Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:53:22.9995381Z         
2025-03-14T04:53:22.9995771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:22.9996289Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:22.9996637Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:53:22.9996868Z         
2025-03-14T04:53:22.9997284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:22.9997808Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:22.9998066Z         
2025-03-14T04:53:22.9998473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:22.9998999Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:22.9999240Z         
2025-03-14T04:53:22.9999669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:23.0000219Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:23.0000538Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:53:23.0000868Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:23.0001218Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:53:23.0001478Z         
2025-03-14T04:53:23.0001914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:23.0002461Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:23.0002778Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:53:23.0003106Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:23.0003463Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:53:23.0003713Z         
2025-03-14T04:53:23.0004158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:23.0004646Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:23.0004963Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:23.0005320Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:53:23.0005567Z         
2025-03-14T04:53:23.0005978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:23.0006481Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:23.0006812Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:23.0007163Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:53:23.0007432Z         
2025-03-14T04:53:23.0007833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:23.0008299Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:23.0008555Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:23.0008790Z         
2025-03-14T04:53:23.0009187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:23.0009652Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:23.0009918Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:23.0010155Z         
2025-03-14T04:53:23.0010546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:23.0011028Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:23.0011336Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:23.0011675Z         
2025-03-14T04:53:23.0012087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:23.0012586Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:23.0012872Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:23.0013153Z         
2025-03-14T04:53:23.0013562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:23.0014128Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:23.0014426Z         
2025-03-14T04:53:23.0014852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:23.0015425Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:53:23.0015717Z         
2025-03-14T04:53:23.0016206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:53:23.0016839Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:53:23.0017133Z         
2025-03-14T04:53:23.0017742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:53:23.0018447Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:53:23.0018730Z         
2025-03-14T04:53:23.0019131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:23.0019648Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:53:23.0019918Z         
2025-03-14T04:53:23.0020464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:53:23.0021113Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:53:23.0021397Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:53:23.0021675Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:53:23.0021910Z         
2025-03-14T04:53:23.0022476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:53:23.0023177Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:53:23.0023647Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:53:23.0024007Z         
2025-03-14T04:53:23.0024579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:53:23.0025239Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:53:23.0025509Z         
2025-03-14T04:53:23.0025881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:23.0026367Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:53:23.0026631Z         
2025-03-14T04:53:23.0027103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:53:23.0027679Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:53:23.0027937Z         
2025-03-14T04:53:23.0028316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:23.0028802Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:53:23.0029062Z         
2025-03-14T04:53:23.0029514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:53:23.0030074Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:53:23.0030327Z         
2025-03-14T04:53:23.0030890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:53:23.0031550Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:53:23.0031870Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:23.0032195Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:53:23.0032557Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:53:23.0032816Z         
2025-03-14T04:53:23.0033274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:53:23.0033820Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:53:23.0034071Z         
2025-03-14T04:53:25.9912369Z 
2025-03-14T04:53:25.9917448Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:25.9922064Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"):
2025-03-14T04:53:25.9926092Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T04:53:25.9926449Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T04:53:25.9926735Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T04:53:25.9927007Z         
2025-03-14T04:53:25.9927564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:53:25.9928263Z         pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T04:53:25.9928618Z         
2025-03-14T04:53:25.9929160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:53:25.9929855Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T04:53:25.9930255Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:53:25.9930603Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:53:25.9930865Z         
2025-03-14T04:53:25.9931340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:25.9932383Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T04:53:25.9932707Z         
2025-03-14T04:53:25.9933136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:25.9933673Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:53:25.9933958Z         
2025-03-14T04:53:25.9934360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:25.9934867Z         getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:25.9935184Z         getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:25.9935513Z         widths: "f32[328500][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T04:53:25.9935782Z         
2025-03-14T04:53:25.9936198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:25.9936702Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:25.9937063Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:25.9937393Z         heights: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:53:25.9937663Z         
2025-03-14T04:53:25.9938122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:25.9938618Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:25.9938891Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:53:25.9939155Z         ctr_x: "f32[328500][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T04:53:25.9939447Z         
2025-03-14T04:53:25.9939867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:25.9940399Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:25.9940705Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:53:25.9940993Z         ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T04:53:25.9941247Z         
2025-03-14T04:53:25.9941674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:25.9942190Z         getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:25.9942532Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T04:53:25.9942778Z         
2025-03-14T04:53:25.9943182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:25.9943732Z         getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:25.9944072Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T04:53:25.9944312Z         
2025-03-14T04:53:25.9944712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:25.9945230Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:25.9945563Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:53:25.9945802Z         
2025-03-14T04:53:25.9946221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:25.9946775Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:25.9947131Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:53:25.9947361Z         
2025-03-14T04:53:25.9947797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:25.9948350Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:25.9948616Z         
2025-03-14T04:53:25.9949046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:25.9949587Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:25.9949849Z         
2025-03-14T04:53:25.9950292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:25.9950869Z         getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:25.9951201Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T04:53:25.9951559Z         getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:25.9951918Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T04:53:25.9952181Z         
2025-03-14T04:53:25.9952620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:25.9953186Z         getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:25.9953507Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T04:53:25.9953838Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:25.9954189Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T04:53:25.9954449Z         
2025-03-14T04:53:25.9954874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:25.9955381Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:25.9955713Z         getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:25.9956071Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T04:53:25.9956328Z         
2025-03-14T04:53:25.9956754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:25.9957262Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:25.9957596Z         getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:25.9957955Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T04:53:25.9958214Z         
2025-03-14T04:53:25.9958627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:25.9959093Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:25.9959378Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:25.9959623Z         
2025-03-14T04:53:25.9960025Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:25.9960492Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:25.9961045Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:25.9961302Z         
2025-03-14T04:53:25.9961704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:25.9962185Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:25.9962484Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:25.9962733Z         
2025-03-14T04:53:25.9963122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:25.9963598Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:25.9963919Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:25.9964172Z         
2025-03-14T04:53:25.9964608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:25.9965212Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:25.9965506Z         
2025-03-14T04:53:25.9965920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:25.9966499Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:53:25.9966784Z         
2025-03-14T04:53:25.9967252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:53:25.9967858Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:53:25.9968151Z         
2025-03-14T04:53:25.9970472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:53:25.9971219Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:53:25.9971525Z         
2025-03-14T04:53:25.9971936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:25.9972431Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:53:25.9972687Z         
2025-03-14T04:53:25.9973218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:53:25.9973873Z         topk = l_pred_objectness_logits_0_.topk(6000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T04:53:25.9974200Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:53:25.9974466Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:53:25.9974692Z         
2025-03-14T04:53:25.9975299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:53:25.9975971Z         getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:53:25.9976420Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)];  proposals_i_1 = getitem_18 = topk_idx = None
2025-03-14T04:53:25.9976762Z         
2025-03-14T04:53:25.9977300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:53:25.9977970Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:53:25.9978250Z         
2025-03-14T04:53:25.9978623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:25.9979114Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:53:25.9979378Z         
2025-03-14T04:53:25.9979858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:53:25.9980440Z         getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:53:25.9980736Z         
2025-03-14T04:53:25.9981116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:25.9981608Z         tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32);  getitem_20 = None
2025-03-14T04:53:25.9981871Z         
2025-03-14T04:53:25.9982331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:53:25.9982939Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:53:25.9983199Z         
2025-03-14T04:53:25.9983766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:53:25.9984430Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T04:53:25.9984737Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:25.9985066Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:53:25.9985409Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:53:25.9985668Z         
2025-03-14T04:53:25.9986117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:53:25.9986657Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:53:25.9986888Z         
2025-03-14T04:53:32.5272681Z 
2025-03-14T04:53:32.5273560Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:32.5276013Z     def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"):
2025-03-14T04:53:32.5278105Z         l_stack0_ = L_stack0_
2025-03-14T04:53:32.5278451Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:53:32.5278925Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:53:32.5279489Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:53:32.5279955Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:53:32.5280562Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:53:32.5281125Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:53:32.5281734Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:53:32.5282286Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:53:32.5282758Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5283200Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5283594Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5284009Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5284324Z         
2025-03-14T04:53:32.5284720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:53:32.5285204Z         x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:53:32.5285914Z         x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:53:32.5286633Z         x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:53:32.5287346Z         x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:53:32.5288053Z         x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:53:32.5288332Z         
2025-03-14T04:53:32.5288734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:53:32.5289709Z         scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:53:32.5290424Z         
2025-03-14T04:53:32.5290840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:53:32.5291924Z         proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:53:32.5292762Z         
2025-03-14T04:53:32.5293149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:32.5293631Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:32.5293883Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:53:32.5294112Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:53:32.5294382Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:32.5294675Z         getitem_2: "Sym(1225 - s0)" = size_1[0]
2025-03-14T04:53:32.5294934Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:53:32.5295162Z         
2025-03-14T04:53:32.5295569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:53:32.5296555Z         proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:53:32.5297325Z         
2025-03-14T04:53:32.5297801Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:32.5298403Z         deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:53:32.5298689Z         
2025-03-14T04:53:32.5299102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:32.5299643Z         boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:53:32.5299928Z         
2025-03-14T04:53:32.5300341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:32.5300860Z         getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:32.5301176Z         getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:32.5301505Z         widths: "f32[3225][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:32.5301780Z         
2025-03-14T04:53:32.5302195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:32.5302704Z         getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:32.5303007Z         getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:32.5303328Z         heights: "f32[3225][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:53:32.5303604Z         
2025-03-14T04:53:32.5304037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:32.5304523Z         getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:32.5304783Z         mul: "f32[3225][1]cpu" = 0.5 * widths
2025-03-14T04:53:32.5305040Z         ctr_x: "f32[3225][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:53:32.5305275Z         
2025-03-14T04:53:32.5305671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:32.5306175Z         getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:32.5306457Z         mul_1: "f32[3225][1]cpu" = 0.5 * heights
2025-03-14T04:53:32.5306716Z         ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:53:32.5306956Z         
2025-03-14T04:53:32.5307379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:32.5307887Z         getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:32.5308230Z         dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:53:32.5308461Z         
2025-03-14T04:53:32.5308840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:32.5309357Z         getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:32.5309676Z         dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:53:32.5309899Z         
2025-03-14T04:53:32.5310279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:32.5310797Z         getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:32.5311113Z         dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:53:32.5311342Z         
2025-03-14T04:53:32.5311728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:32.5312259Z         getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:32.5312607Z         dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:53:32.5312841Z         
2025-03-14T04:53:32.5313259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:32.5313788Z         dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:32.5314046Z         
2025-03-14T04:53:32.5314462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:32.5314983Z         dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:32.5315231Z         
2025-03-14T04:53:32.5315657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:32.5316191Z         getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:32.5316518Z         mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:53:32.5316888Z         getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:32.5317234Z         pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:53:32.5317487Z         
2025-03-14T04:53:32.5317916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:32.5318453Z         getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:32.5318773Z         mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:53:32.5319102Z         getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:32.5319458Z         pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:53:32.5319711Z         
2025-03-14T04:53:32.5320129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:32.5320627Z         exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:32.5321004Z         getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:32.5321346Z         pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:53:32.5321594Z         
2025-03-14T04:53:32.5322011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:32.5322530Z         exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:32.5322845Z         getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:32.5323187Z         pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:53:32.5323455Z         
2025-03-14T04:53:32.5323850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:32.5324302Z         mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:32.5324567Z         x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:32.5324801Z         
2025-03-14T04:53:32.5325189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:32.5325645Z         mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:32.5325910Z         y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:32.5326145Z         
2025-03-14T04:53:32.5326540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:32.5327023Z         mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:32.5327313Z         x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:32.5327563Z         
2025-03-14T04:53:32.5327945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:32.5328413Z         mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:32.5328687Z         y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:32.5328926Z         
2025-03-14T04:53:32.5329361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:32.5329985Z         pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:32.5330279Z         
2025-03-14T04:53:32.5330708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:32.5331270Z         predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320));  pred_boxes = None
2025-03-14T04:53:32.5331713Z         
2025-03-14T04:53:32.5332186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:53:32.5332839Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:53:32.5333204Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:53:32.5333497Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:53:32.5333802Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:53:32.5334116Z         getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:53:32.5334376Z         
2025-03-14T04:53:32.5334787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:32.5335326Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:53:32.5335678Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:53:32.5335909Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:53:32.5336258Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:53:32.5336604Z         getitem_26: "Sym(1225 - s0)" = size_3[0]
2025-03-14T04:53:32.5336862Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:53:32.5337069Z         
2025-03-14T04:53:32.5337485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:53:32.5338029Z         probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:53:32.5338308Z         
2025-03-14T04:53:32.5338744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:53:32.5339337Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:53:32.5339692Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:53:32.5339981Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:53:32.5340275Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:53:32.5340583Z         getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:53:32.5340840Z         
2025-03-14T04:53:32.5341384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:32.5342076Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:53:32.5342409Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:32.5342737Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:53:32.5343087Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:32.5343383Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:32.5343615Z         
2025-03-14T04:53:32.5344044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:32.5344556Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:32.5344782Z         
2025-03-14T04:53:32.5344918Z 
2025-03-14T04:53:32.5345013Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:32.5346941Z     def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"):
2025-03-14T04:53:32.5349013Z         l_stack0_ = L_stack0_
2025-03-14T04:53:32.5349360Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:53:32.5349844Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:53:32.5350335Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:53:32.5350801Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:53:32.5351316Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:53:32.5351852Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:53:32.5352389Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:53:32.5352919Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:53:32.5353377Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5353761Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5354148Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5354532Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5354823Z         
2025-03-14T04:53:32.5355179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:53:32.5355632Z         x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:53:32.5356332Z         x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:53:32.5357050Z         x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:53:32.5357774Z         x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:53:32.5358489Z         x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:53:32.5358764Z         
2025-03-14T04:53:32.5359163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:53:32.5360111Z         scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:53:32.5361347Z         
2025-03-14T04:53:32.5361768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:53:32.5362802Z         proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:53:32.5363547Z         
2025-03-14T04:53:32.5363931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:32.5364418Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:32.5364677Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:53:32.5364959Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:53:32.5365323Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:32.5365879Z         getitem_2: "Sym(1225 - s0)" = size_1[0]
2025-03-14T04:53:32.5366216Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:53:32.5366522Z         
2025-03-14T04:53:32.5366964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:53:32.5367972Z         proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:53:32.5396647Z         
2025-03-14T04:53:32.5397379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:32.5397978Z         deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:53:32.5398253Z         
2025-03-14T04:53:32.5398670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:32.5399210Z         boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:53:32.5399490Z         
2025-03-14T04:53:32.5400022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:32.5400547Z         getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:32.5400867Z         getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:32.5401201Z         widths: "f32[3225][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:32.5401472Z         
2025-03-14T04:53:32.5401896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:32.5402407Z         getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:32.5402698Z         getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:32.5403009Z         heights: "f32[3225][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:53:32.5403274Z         
2025-03-14T04:53:32.5403675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:32.5404197Z         getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:32.5404456Z         mul: "f32[3225][1]cpu" = 0.5 * widths
2025-03-14T04:53:32.5404712Z         ctr_x: "f32[3225][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:53:32.5404986Z         
2025-03-14T04:53:32.5405388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:32.5405910Z         getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:32.5406202Z         mul_1: "f32[3225][1]cpu" = 0.5 * heights
2025-03-14T04:53:32.5406472Z         ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:53:32.5406744Z         
2025-03-14T04:53:32.5407162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:32.5407688Z         getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:32.5408021Z         dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:53:32.5408261Z         
2025-03-14T04:53:32.5408654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:32.5409166Z         getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:32.5409489Z         dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:53:32.5409721Z         
2025-03-14T04:53:32.5410113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:32.5410627Z         getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:32.5410953Z         dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:53:32.5411186Z         
2025-03-14T04:53:32.5411672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:32.5412238Z         getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:32.5412613Z         dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:53:32.5412874Z         
2025-03-14T04:53:32.5413385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:32.5413967Z         dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:32.5414221Z         
2025-03-14T04:53:32.5414643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:32.5415167Z         dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:32.5415427Z         
2025-03-14T04:53:32.5415874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:32.5416437Z         getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:32.5416757Z         mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:53:32.5417097Z         getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:32.5417449Z         pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:53:32.5417708Z         
2025-03-14T04:53:32.5418164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:32.5418731Z         getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:32.5419052Z         mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:53:32.5419385Z         getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:32.5419729Z         pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:53:32.5420008Z         
2025-03-14T04:53:32.5420436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:32.5420943Z         exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:32.5421271Z         getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:32.5421617Z         pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:53:32.5421872Z         
2025-03-14T04:53:32.5422295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:32.5422805Z         exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:32.5423139Z         getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:32.5423495Z         pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:53:32.5423751Z         
2025-03-14T04:53:32.5424158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:32.5424611Z         mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:32.5424867Z         x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:32.5425102Z         
2025-03-14T04:53:32.5425487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:32.5425935Z         mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:32.5426188Z         y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:32.5426433Z         
2025-03-14T04:53:32.5426813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:32.5427288Z         mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:32.5427575Z         x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:32.5427819Z         
2025-03-14T04:53:32.5428201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:32.5428674Z         mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:32.5428958Z         y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:32.5429201Z         
2025-03-14T04:53:32.5429648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:32.5430248Z         pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:32.5430542Z         
2025-03-14T04:53:32.5431090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:32.5431879Z         predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320));  pred_boxes = None
2025-03-14T04:53:32.5432187Z         
2025-03-14T04:53:32.5432636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:53:32.5433425Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:53:32.5433978Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:53:32.5434274Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:53:32.5434589Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:53:32.5434928Z         getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:53:32.5435197Z         
2025-03-14T04:53:32.5435592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:32.5436174Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:53:32.5436519Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:53:32.5436760Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:53:32.5437124Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:53:32.5437476Z         getitem_26: "Sym(1225 - s0)" = size_3[0]
2025-03-14T04:53:32.5437718Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:53:32.5437935Z         
2025-03-14T04:53:32.5438352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:53:32.5439065Z         probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:53:32.5439364Z         
2025-03-14T04:53:32.5439805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:53:32.5440401Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:53:32.5440782Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:53:32.5441071Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:53:32.5441358Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:53:32.5441673Z         getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:53:32.5441929Z         
2025-03-14T04:53:32.5442476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:32.5443169Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:53:32.5443503Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:32.5443836Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:53:32.5444171Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:32.5444457Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:32.5444693Z         
2025-03-14T04:53:32.5445147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:32.5445672Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:32.5445900Z         
2025-03-14T04:53:32.5446072Z 
2025-03-14T04:53:32.5446158Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:32.5448104Z     def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"):
2025-03-14T04:53:32.5450244Z         l_stack0_ = L_stack0_
2025-03-14T04:53:32.5450596Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:53:32.5451089Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:53:32.5451630Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:53:32.5452128Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:53:32.5452691Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:53:32.5453307Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:53:32.5453903Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:53:32.5454509Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:53:32.5455006Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5455425Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5455832Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5456232Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5456532Z         
2025-03-14T04:53:32.5456915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:53:32.5457403Z         x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:53:32.5458137Z         x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:53:32.5458907Z         x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:53:32.5459662Z         x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:53:32.5460417Z         x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:53:32.5460931Z         
2025-03-14T04:53:32.5461354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:53:32.5462426Z         scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:53:32.5463163Z         
2025-03-14T04:53:32.5463567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:53:32.5464566Z         proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:53:32.5465299Z         
2025-03-14T04:53:32.5465667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:32.5466127Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:32.5466379Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:53:32.5466608Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:53:32.5466875Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:32.5467132Z         getitem_2: "Sym(1225 - s0)" = size_1[0]
2025-03-14T04:53:32.5467375Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:53:32.5467591Z         
2025-03-14T04:53:32.5467964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:53:32.5468920Z         proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:53:32.5469631Z         
2025-03-14T04:53:32.5470087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:32.5470664Z         deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:53:32.5470932Z         
2025-03-14T04:53:32.5471323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:32.5471839Z         boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:53:32.5472110Z         
2025-03-14T04:53:32.5472503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:32.5473029Z         getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:32.5473333Z         getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:32.5473645Z         widths: "f32[3225][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:32.5473926Z         
2025-03-14T04:53:32.5474320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:32.5474805Z         getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:32.5475093Z         getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:32.5475417Z         heights: "f32[3225][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:53:32.5475674Z         
2025-03-14T04:53:32.5476066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:32.5476550Z         getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:32.5476801Z         mul: "f32[3225][1]cpu" = 0.5 * widths
2025-03-14T04:53:32.5477054Z         ctr_x: "f32[3225][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:53:32.5477283Z         
2025-03-14T04:53:32.5477671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:32.5478170Z         getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:32.5478442Z         mul_1: "f32[3225][1]cpu" = 0.5 * heights
2025-03-14T04:53:32.5478701Z         ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:53:32.5478936Z         
2025-03-14T04:53:32.5479335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:32.5479850Z         getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:32.5480171Z         dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:53:32.5480402Z         
2025-03-14T04:53:32.5480780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:32.5481283Z         getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:32.5481612Z         dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:53:32.5481845Z         
2025-03-14T04:53:32.5482225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:32.5482722Z         getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:32.5483036Z         dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:53:32.5483266Z         
2025-03-14T04:53:32.5483647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:32.5484178Z         getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:32.5484517Z         dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:53:32.5484748Z         
2025-03-14T04:53:32.5485162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:32.5485701Z         dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:32.5485950Z         
2025-03-14T04:53:32.5486359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:32.5486890Z         dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:32.5487139Z         
2025-03-14T04:53:32.5487582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:32.5488134Z         getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:32.5488464Z         mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:53:32.5488791Z         getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:32.5489141Z         pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:53:32.5489406Z         
2025-03-14T04:53:32.5489861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:32.5490429Z         getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:32.5490752Z         mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:53:32.5491089Z         getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:32.5491525Z         pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:53:32.5491825Z         
2025-03-14T04:53:32.5492323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:32.5492878Z         exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:32.5493211Z         getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:32.5493557Z         pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:53:32.5493807Z         
2025-03-14T04:53:32.5494237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:32.5494779Z         exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:32.5495122Z         getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:32.5495481Z         pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:53:32.5495744Z         
2025-03-14T04:53:32.5496155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:32.5496639Z         mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:32.5496910Z         x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:32.5497144Z         
2025-03-14T04:53:32.5497551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:32.5498022Z         mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:32.5498290Z         y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:32.5498531Z         
2025-03-14T04:53:32.5498951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:32.5499454Z         mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:32.5499758Z         x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:32.5500036Z         
2025-03-14T04:53:32.5500445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:32.5500937Z         mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:32.5501251Z         y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:32.5501524Z         
2025-03-14T04:53:32.5501981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:32.5502566Z         pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:32.5502858Z         
2025-03-14T04:53:32.5503281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:32.5503844Z         predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320));  pred_boxes = None
2025-03-14T04:53:32.5504133Z         
2025-03-14T04:53:32.5504586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:53:32.5505191Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:53:32.5505551Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:53:32.5505836Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:53:32.5506138Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:53:32.5506448Z         getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:53:32.5506707Z         
2025-03-14T04:53:32.5507073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:32.5507619Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:53:32.5507965Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:53:32.5508219Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:53:32.5508583Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:53:32.5508928Z         getitem_26: "Sym(1225 - s0)" = size_3[0]
2025-03-14T04:53:32.5509169Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:53:32.5509384Z         
2025-03-14T04:53:32.5509801Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:53:32.5510356Z         probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:53:32.5510637Z         
2025-03-14T04:53:32.5511067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:53:32.5511664Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:53:32.5512019Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:53:32.5512305Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:53:32.5512613Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:53:32.5512922Z         getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:53:32.5513205Z         
2025-03-14T04:53:32.5513748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:32.5514435Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:53:32.5514791Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:32.5515125Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:53:32.5515463Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:32.5515754Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:32.5515988Z         
2025-03-14T04:53:32.5516424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:32.5516949Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:32.5517183Z         
2025-03-14T04:53:32.5517272Z 
2025-03-14T04:53:32.5517359Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:32.5519298Z     def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"):
2025-03-14T04:53:32.5521359Z         l_stack0_ = L_stack0_
2025-03-14T04:53:32.5521704Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:53:32.5522179Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:53:32.5522647Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:53:32.5523117Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:53:32.5523616Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:53:32.5524163Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:53:32.5524707Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:53:32.5525250Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:53:32.5525736Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5526129Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5526533Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5526915Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:53:32.5527206Z         
2025-03-14T04:53:32.5527570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:53:32.5528077Z         x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:53:32.5528770Z         x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:53:32.5529515Z         x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:53:32.5530264Z         x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:53:32.5531023Z         x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:53:32.5531329Z         
2025-03-14T04:53:32.5531838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:53:32.5532934Z         scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:53:32.5533680Z         
2025-03-14T04:53:32.5534109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:53:32.5535145Z         proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:53:32.5535886Z         
2025-03-14T04:53:32.5536275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:32.5536761Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:32.5537024Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:53:32.5537272Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:53:32.5537562Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:32.5537835Z         getitem_2: "Sym(1225 - s0)" = size_1[0]
2025-03-14T04:53:32.5538091Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:53:32.5538321Z         
2025-03-14T04:53:32.5538710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:53:32.5539732Z         proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:53:32.5540485Z         
2025-03-14T04:53:32.5540952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:32.5541571Z         deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:53:32.5541853Z         
2025-03-14T04:53:32.5542267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:32.5542836Z         boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:53:32.5543122Z         
2025-03-14T04:53:32.5543544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:32.5544067Z         getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:32.5544392Z         getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:32.5544726Z         widths: "f32[3225][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:32.5545007Z         
2025-03-14T04:53:32.5545433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:32.5545956Z         getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:32.5546271Z         getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:32.5546596Z         heights: "f32[3225][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:53:32.5546875Z         
2025-03-14T04:53:32.5547306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:32.5547822Z         getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:32.5548101Z         mul: "f32[3225][1]cpu" = 0.5 * widths
2025-03-14T04:53:32.5548370Z         ctr_x: "f32[3225][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:53:32.5548624Z         
2025-03-14T04:53:32.5549046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:32.5549598Z         getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:32.5549896Z         mul_1: "f32[3225][1]cpu" = 0.5 * heights
2025-03-14T04:53:32.5550155Z         ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:53:32.5550391Z         
2025-03-14T04:53:32.5550783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:32.5551291Z         getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:32.5551610Z         dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:53:32.5551840Z         
2025-03-14T04:53:32.5552220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:32.5552721Z         getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:32.5553039Z         dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:53:32.5553268Z         
2025-03-14T04:53:32.5553668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:32.5554173Z         getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:32.5554505Z         dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:53:32.5554735Z         
2025-03-14T04:53:32.5555118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:32.5555652Z         getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:32.5556007Z         dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:53:32.5556236Z         
2025-03-14T04:53:32.5556665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:32.5557211Z         dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:32.5557478Z         
2025-03-14T04:53:32.5557913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:32.5558426Z         dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:32.5558672Z         
2025-03-14T04:53:32.5559095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:32.5559631Z         getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:32.5559954Z         mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:53:32.5560297Z         getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:32.5560810Z         pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:53:32.5561088Z         
2025-03-14T04:53:32.5561548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:32.5562108Z         getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:32.5562476Z         mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:53:32.5562817Z         getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:32.5563151Z         pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:53:32.5563397Z         
2025-03-14T04:53:32.5563813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:32.5564341Z         exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:32.5564675Z         getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:32.5565021Z         pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:53:32.5565281Z         
2025-03-14T04:53:32.5565717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:32.5566237Z         exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:32.5566581Z         getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:32.5566980Z         pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:53:32.5567248Z         
2025-03-14T04:53:32.5567669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:32.5568173Z         mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:32.5568444Z         x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:32.5568689Z         
2025-03-14T04:53:32.5569106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:32.5569610Z         mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:32.5569884Z         y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:32.5570141Z         
2025-03-14T04:53:32.5570572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:32.5571094Z         mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:32.5571413Z         x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:32.5571751Z         
2025-03-14T04:53:32.5572200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:32.5572727Z         mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:32.5573027Z         y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:32.5573286Z         
2025-03-14T04:53:32.5573745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:32.5574364Z         pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:32.5574669Z         
2025-03-14T04:53:32.5575105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:32.5575692Z         predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320));  pred_boxes = None
2025-03-14T04:53:32.5575985Z         
2025-03-14T04:53:32.5576484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:53:32.5577131Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:53:32.5577517Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:53:32.5577820Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:53:32.5578136Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:53:32.5578470Z         getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:53:32.5578744Z         
2025-03-14T04:53:32.5579138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:32.5579713Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:53:32.5580078Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:53:32.5580327Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:53:32.5580722Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:53:32.5581083Z         getitem_26: "Sym(1225 - s0)" = size_3[0]
2025-03-14T04:53:32.5581317Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:53:32.5581547Z         
2025-03-14T04:53:32.5581951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:53:32.5582486Z         probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:53:32.5582769Z         
2025-03-14T04:53:32.5583203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:53:32.5583813Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:53:32.5584172Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:53:32.5584456Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:53:32.5584749Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:53:32.5585062Z         getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:53:32.5585320Z         
2025-03-14T04:53:32.5585867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:32.5586546Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:53:32.5586883Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:32.5587219Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:53:32.5587557Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:32.5587848Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:32.5588082Z         
2025-03-14T04:53:32.5588514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:32.5589026Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:32.5589249Z         
2025-03-14T04:53:34.4072924Z 
2025-03-14T04:53:34.4077098Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:34.4078107Z     def forward(self, L_predictions_0_: "f32[3225, 81][81, 1]cpu", L_predictions_1_: "f32[3225, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"):
2025-03-14T04:53:34.4079662Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:53:34.4079907Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:53:34.4080284Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:53:34.4085877Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:53:34.4087062Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:53:34.4087538Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:53:34.4087840Z         
2025-03-14T04:53:34.4088278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:34.4089209Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:34.4089477Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:53:34.4089711Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:53:34.4090065Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:34.4090342Z         getitem_2: "Sym(1225 - s0)" = size_1[0]
2025-03-14T04:53:34.4090586Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:53:34.4090806Z         
2025-03-14T04:53:34.4091188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:53:34.4092345Z         proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:53:34.4093119Z         
2025-03-14T04:53:34.4093592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:34.4094178Z         deltas: "f32[3225, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:53:34.4094453Z         
2025-03-14T04:53:34.4094848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:34.4095379Z         boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:53:34.4095653Z         
2025-03-14T04:53:34.4096054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:34.4096552Z         getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:34.4096852Z         getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:34.4097169Z         widths: "f32[3225][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:34.4097430Z         
2025-03-14T04:53:34.4097831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:34.4098329Z         getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:34.4098661Z         getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:34.4098978Z         heights: "f32[3225][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:53:34.4099241Z         
2025-03-14T04:53:34.4099634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:34.4100116Z         getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:34.4100371Z         mul: "f32[3225][1]cpu" = 0.5 * widths
2025-03-14T04:53:34.4100623Z         ctr_x: "f32[3225][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:53:34.4100859Z         
2025-03-14T04:53:34.4101252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:34.4101753Z         getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:34.4102035Z         mul_1: "f32[3225][1]cpu" = 0.5 * heights
2025-03-14T04:53:34.4102327Z         ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:53:34.4102576Z         
2025-03-14T04:53:34.4103010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:34.4103516Z         getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:34.4103860Z         dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:53:34.4104083Z         
2025-03-14T04:53:34.4104455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:34.4104946Z         getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:34.4105275Z         dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:53:34.4105499Z         
2025-03-14T04:53:34.4105872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:34.4106355Z         getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:34.4106668Z         dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:53:34.4106893Z         
2025-03-14T04:53:34.4107267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:34.4107790Z         getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:34.4108125Z         dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:53:34.4108353Z         
2025-03-14T04:53:34.4108774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:34.4109297Z         dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:34.4109554Z         
2025-03-14T04:53:34.4109958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:34.4110475Z         dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:34.4110723Z         
2025-03-14T04:53:34.4111165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:34.4111707Z         getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:34.4112021Z         mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:53:34.4112350Z         getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:34.4112693Z         pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:53:34.4112949Z         
2025-03-14T04:53:34.4113585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:34.4114159Z         getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:34.4114475Z         mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:53:34.4114809Z         getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:34.4115165Z         pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:53:34.4115433Z         
2025-03-14T04:53:34.4115892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:34.4116424Z         exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:34.4116786Z         getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:34.4117157Z         pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:53:34.4117424Z         
2025-03-14T04:53:34.4117872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:34.4118402Z         exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:34.4118739Z         getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:34.4119142Z         pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:53:34.4119540Z         
2025-03-14T04:53:34.4119966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:34.4120460Z         mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:34.4120737Z         x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:34.4120988Z         
2025-03-14T04:53:34.4121402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:34.4121887Z         mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:34.4122177Z         y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:34.4122550Z         
2025-03-14T04:53:34.4122974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:34.4123484Z         mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:34.4123788Z         x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:34.4124051Z         
2025-03-14T04:53:34.4124461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:34.4124962Z         mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:34.4125283Z         y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:34.4125538Z         
2025-03-14T04:53:34.4125984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:34.4126596Z         pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:34.4126890Z         
2025-03-14T04:53:34.4127325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:34.4127905Z         predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320));  pred_boxes = None
2025-03-14T04:53:34.4128203Z         
2025-03-14T04:53:34.4128672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:53:34.4129321Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:53:34.4129702Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:53:34.4130023Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:53:34.4130347Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:53:34.4130671Z         getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:53:34.4130958Z         
2025-03-14T04:53:34.4131351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:34.4132015Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:53:34.4132426Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:53:34.4132670Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:53:34.4133043Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:53:34.4133399Z         getitem_26: "Sym(1225 - s0)" = size_3[0]
2025-03-14T04:53:34.4133650Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:53:34.4133873Z         
2025-03-14T04:53:34.4134301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:53:34.4134921Z         probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:53:34.4135249Z         
2025-03-14T04:53:34.4135704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:53:34.4136320Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:53:34.4136685Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:53:34.4136981Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:53:34.4137281Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:53:34.4137605Z         getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:53:34.4137868Z         
2025-03-14T04:53:34.4138435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:34.4139176Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:53:34.4139540Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:34.4139887Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:53:34.4140236Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:34.4140535Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:34.4140778Z         
2025-03-14T04:53:34.4141219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:34.4141747Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:34.4141987Z         
2025-03-14T04:53:34.4142124Z 
2025-03-14T04:53:34.4142213Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:34.4143097Z     def forward(self, L_predictions_0_: "f32[3225, 81][81, 1]cpu", L_predictions_1_: "f32[3225, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"):
2025-03-14T04:53:34.4143917Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:53:34.4144156Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:53:34.4144490Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:53:34.4144885Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:53:34.4145275Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:53:34.4145665Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:53:34.4145970Z         
2025-03-14T04:53:34.4146349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:34.4146811Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:34.4147064Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:53:34.4147293Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:53:34.4147569Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:34.4147826Z         getitem_2: "Sym(1225 - s0)" = size_1[0]
2025-03-14T04:53:34.4148069Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:53:34.4148283Z         
2025-03-14T04:53:34.4148650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:53:34.4149606Z         proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:53:34.4150329Z         
2025-03-14T04:53:34.4150793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:34.4151367Z         deltas: "f32[3225, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:53:34.4151705Z         
2025-03-14T04:53:34.4152320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:34.4152902Z         boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:53:34.4153180Z         
2025-03-14T04:53:34.4153585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:34.4154240Z         getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:34.4154548Z         getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:34.4154878Z         widths: "f32[3225][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:34.4155243Z         
2025-03-14T04:53:34.4155652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:34.4156155Z         getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:34.4156458Z         getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:34.4156788Z         heights: "f32[3225][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:53:34.4157192Z         
2025-03-14T04:53:34.4157733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:34.4158237Z         getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:34.4158499Z         mul: "f32[3225][1]cpu" = 0.5 * widths
2025-03-14T04:53:34.4158781Z         ctr_x: "f32[3225][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:53:34.4159028Z         
2025-03-14T04:53:34.4159535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:34.4160051Z         getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:34.4160359Z         mul_1: "f32[3225][1]cpu" = 0.5 * heights
2025-03-14T04:53:34.4160779Z         ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:53:34.4161034Z         
2025-03-14T04:53:34.4161448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:34.4161965Z         getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:34.4162301Z         dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:53:34.4162532Z         
2025-03-14T04:53:34.4162911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:34.4163409Z         getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:34.4163730Z         dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:53:34.4163959Z         
2025-03-14T04:53:34.4164339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:34.4164844Z         getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:34.4165158Z         dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:53:34.4165394Z         
2025-03-14T04:53:34.4165770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:34.4166284Z         getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:34.4166611Z         dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:53:34.4167447Z         
2025-03-14T04:53:34.4167873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:34.4168403Z         dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:34.4168661Z         
2025-03-14T04:53:34.4169083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:34.4169624Z         dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:34.4169879Z         
2025-03-14T04:53:34.4170313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:34.4170861Z         getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:34.4171176Z         mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:53:34.4171577Z         getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:34.4171992Z         pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:53:34.4172265Z         
2025-03-14T04:53:34.4172728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:34.4173308Z         getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:34.4173631Z         mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:53:34.4173966Z         getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:34.4174341Z         pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:53:34.4174598Z         
2025-03-14T04:53:34.4175022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:34.4175532Z         exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:34.4175858Z         getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:34.4176216Z         pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:53:34.4176461Z         
2025-03-14T04:53:34.4176870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:34.4177371Z         exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:34.4177698Z         getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:34.4178049Z         pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:53:34.4178314Z         
2025-03-14T04:53:34.4178723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:34.4179204Z         mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:34.4179469Z         x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:34.4179711Z         
2025-03-14T04:53:34.4180111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:34.4180621Z         mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:34.4180882Z         y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:34.4181112Z         
2025-03-14T04:53:34.4181496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:34.4181973Z         mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:34.4182257Z         x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:34.4182502Z         
2025-03-14T04:53:34.4182883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:34.4183353Z         mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:34.4183632Z         y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:34.4183876Z         
2025-03-14T04:53:34.4184299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:34.4184896Z         pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:34.4185183Z         
2025-03-14T04:53:34.4185599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:34.4186187Z         predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320));  pred_boxes = None
2025-03-14T04:53:34.4186473Z         
2025-03-14T04:53:34.4186917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:53:34.4187546Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:53:34.4187912Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:53:34.4188204Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:53:34.4188509Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:53:34.4188827Z         getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:53:34.4189092Z         
2025-03-14T04:53:34.4189474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:34.4190047Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:53:34.4190402Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:53:34.4190649Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:53:34.4191018Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:53:34.4191378Z         getitem_26: "Sym(1225 - s0)" = size_3[0]
2025-03-14T04:53:34.4191615Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:53:34.4191834Z         
2025-03-14T04:53:34.4192250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:53:34.4192847Z         probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:53:34.4193174Z         
2025-03-14T04:53:34.4193629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:53:34.4194239Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:53:34.4194601Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:53:34.4194893Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:53:34.4195191Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:53:34.4195501Z         getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:53:34.4195761Z         
2025-03-14T04:53:34.4196303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:34.4196991Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:53:34.4197324Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:34.4197655Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:53:34.4198007Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:34.4198302Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:34.4198534Z         
2025-03-14T04:53:34.4198977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:34.4199510Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:34.4199736Z         
2025-03-14T04:53:34.4199865Z 
2025-03-14T04:53:34.4199957Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:34.4200772Z     def forward(self, L_predictions_0_: "f32[3225, 81][81, 1]cpu", L_predictions_1_: "f32[3225, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"):
2025-03-14T04:53:34.4201585Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:53:34.4201810Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:53:34.4202122Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:53:34.4202521Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:53:34.4202914Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:53:34.4203301Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:53:34.4203588Z         
2025-03-14T04:53:34.4203954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:34.4204417Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:34.4204667Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:53:34.4204897Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:53:34.4205166Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:34.4205423Z         getitem_2: "Sym(1225 - s0)" = size_1[0]
2025-03-14T04:53:34.4205666Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:53:34.4205882Z         
2025-03-14T04:53:34.4206252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:53:34.4207221Z         proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:53:34.4207944Z         
2025-03-14T04:53:34.4208403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:34.4208972Z         deltas: "f32[3225, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:53:34.4209238Z         
2025-03-14T04:53:34.4209632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:34.4210148Z         boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:53:34.4210421Z         
2025-03-14T04:53:34.4210816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:34.4211327Z         getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:34.4211735Z         getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:34.4212072Z         widths: "f32[3225][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:34.4212370Z         
2025-03-14T04:53:34.4212794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:34.4213300Z         getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:34.4213600Z         getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:34.4213934Z         heights: "f32[3225][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:53:34.4214202Z         
2025-03-14T04:53:34.4214608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:34.4215105Z         getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:34.4215364Z         mul: "f32[3225][1]cpu" = 0.5 * widths
2025-03-14T04:53:34.4215624Z         ctr_x: "f32[3225][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:53:34.4215866Z         
2025-03-14T04:53:34.4216268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:34.4216782Z         getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:34.4217065Z         mul_1: "f32[3225][1]cpu" = 0.5 * heights
2025-03-14T04:53:34.4217331Z         ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:53:34.4217574Z         
2025-03-14T04:53:34.4217986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:34.4218507Z         getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:34.4218838Z         dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:53:34.4219078Z         
2025-03-14T04:53:34.4219465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:34.4219977Z         getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:34.4220325Z         dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:53:34.4220554Z         
2025-03-14T04:53:34.4220945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:34.4221454Z         getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:34.4221782Z         dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:53:34.4222019Z         
2025-03-14T04:53:34.4222415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:34.4222965Z         getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:34.4223315Z         dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:53:34.4223556Z         
2025-03-14T04:53:34.4223996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:34.4224535Z         dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:34.4224829Z         
2025-03-14T04:53:34.4225256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:34.4225796Z         dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:34.4226055Z         
2025-03-14T04:53:34.4226490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:34.4227030Z         getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:34.4227370Z         mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:53:34.4227708Z         getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:34.4228063Z         pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:53:34.4228324Z         
2025-03-14T04:53:34.4228769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:34.4229319Z         getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:34.4229639Z         mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:53:34.4229970Z         getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:34.4230313Z         pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:53:34.4230561Z         
2025-03-14T04:53:34.4230970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:34.4231462Z         exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:34.4231776Z         getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:34.4232103Z         pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:53:34.4232343Z         
2025-03-14T04:53:34.4232747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:34.4233253Z         exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:34.4233580Z         getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:34.4233915Z         pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:53:34.4234167Z         
2025-03-14T04:53:34.4234575Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:34.4235042Z         mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:34.4235303Z         x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:34.4235541Z         
2025-03-14T04:53:34.4235926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:34.4236374Z         mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:34.4236627Z         y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:34.4236856Z         
2025-03-14T04:53:34.4237233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:34.4237748Z         mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:34.4238025Z         x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:34.4238302Z         
2025-03-14T04:53:34.4238691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:34.4239163Z         mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:34.4239461Z         y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:34.4239707Z         
2025-03-14T04:53:34.4240166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:34.4240761Z         pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:34.4241041Z         
2025-03-14T04:53:34.4241451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:34.4241989Z         predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320));  pred_boxes = None
2025-03-14T04:53:34.4242265Z         
2025-03-14T04:53:34.4242701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:53:34.4243298Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:53:34.4243656Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:53:34.4243937Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:53:34.4244231Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:53:34.4244535Z         getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:53:34.4244789Z         
2025-03-14T04:53:34.4245154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:34.4245698Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:53:34.4246037Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:53:34.4246287Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:53:34.4246644Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:53:34.4246982Z         getitem_26: "Sym(1225 - s0)" = size_3[0]
2025-03-14T04:53:34.4247220Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:53:34.4247435Z         
2025-03-14T04:53:34.4247842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:53:34.4248427Z         probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:53:34.4248746Z         
2025-03-14T04:53:34.4249181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:53:34.4249792Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:53:34.4250150Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:53:34.4250437Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:53:34.4250753Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:53:34.4251065Z         getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:53:34.4251337Z         
2025-03-14T04:53:34.4251979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:34.4252701Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:53:34.4253069Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:34.4253404Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:53:34.4253741Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:34.4254087Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:34.4254409Z         
2025-03-14T04:53:34.4254938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:34.4255455Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:34.4255686Z         
2025-03-14T04:53:37.1406170Z 
2025-03-14T04:53:37.1406757Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:37.1407204Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T04:53:37.1407577Z         l_scores_0_ = L_scores_0_
2025-03-14T04:53:37.1407794Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:53:37.1407995Z         
2025-03-14T04:53:37.1408629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:37.1409364Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:53:37.1409711Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:37.1410045Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:53:37.1410376Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:37.1410685Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:37.1411257Z         
2025-03-14T04:53:37.1411885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:37.1412476Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:37.1412745Z         
2025-03-14T04:53:37.1412860Z 
2025-03-14T04:53:37.1412949Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:37.1413262Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T04:53:37.1413575Z         l_scores_0_ = L_scores_0_
2025-03-14T04:53:37.1413784Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:53:37.1413976Z         
2025-03-14T04:53:37.1414606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:37.1415353Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:53:37.1415697Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:37.1416098Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:53:37.1416454Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:37.1416756Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:37.1417064Z         
2025-03-14T04:53:37.1417546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:37.1418107Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:37.1418394Z         
2025-03-14T04:53:51.7203236Z Compilation time (from dynamo_timed): 45.955684111
2025-03-14T04:53:51.7208456Z pass
2025-03-14T04:53:51.7208998Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:53:51.7209934Z TIMING: entire_frame_compile:45.95568 gc:0.03183 _recursive_pre_grad_passes:0.03098 async_compile.wait:11.0958 backend_compile:30.80132 _recursive_joint_graph_passes:0.56658 _recursive_post_grad_passes:0.15207 code_gen:15.85088 inductor_compile:18.47933 total_wall_time:45.95568
2025-03-14T04:53:51.7211082Z STATS: call_* op count: 781 | FakeTensorMode.__torch_dispatch__:27912 | FakeTensor.__torch_dispatch__:3330 | ProxyTorchDispatchMode.__torch_dispatch__:10384 | attempt fast:51 | slow no contiguity match:20 | fast is_contiguous:31
2025-03-14T04:53:51.7211833Z Dynamo produced 52 graphs covering 781 ops with 42 graph breaks (6 unique)
2025-03-14T04:53:57.2077043Z 
2025-03-14T04:54:04.8925361Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:54:04.8927271Z loading model: 0it [00:07, ?it/s]
2025-03-14T04:54:04.8940892Z cpu  eval  detectron2_fasterrcnn_r_101_fpn    
2025-03-14T04:54:20.9899861Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_101_fpn. Setting accuracy check to cosine
2025-03-14T04:54:21.0047058Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:54:28.6756913Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:54:36.9797124Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:54:52.7776943Z 
2025-03-14T04:54:52.7777740Z class GraphModule(torch.nn.Module):
2025-03-14T04:54:52.7927229Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T04:54:52.8049073Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:54:52.8049560Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8050340Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8051207Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8052254Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8053186Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8054114Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8055103Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8056177Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8057209Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8061351Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8062334Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8063393Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8064437Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8065473Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8066401Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8067309Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8068200Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8069167Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8070124Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8071059Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8071897Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:54:52.8072773Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8073707Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8074621Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:54:52.8075524Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:54:52.8076338Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8077222Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8078121Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8079008Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8079830Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8080615Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8081434Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8082314Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8083173Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8083995Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8084789Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8085598Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8086461Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8087303Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8088117Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8088895Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8089723Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8090610Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8091573Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8092530Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8093378Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8094284Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8095265Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8096215Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8097124Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8098003Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8098914Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8099906Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8100799Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8101613Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8102391Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8103236Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8104155Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8104998Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8105846Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8106667Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8107553Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8108405Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8109244Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8110056Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8110838Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8111647Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8112514Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8113381Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8114202Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8114994Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:54:52.8115841Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8116744Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8117625Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:54:52.8118489Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:54:52.8119311Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8120121Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8120999Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8121839Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8122640Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8123405Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8124190Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8125034Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8125851Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8126645Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8127437Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8128254Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8129130Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8129980Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8130811Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8131714Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8132592Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8133548Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8134375Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8135192Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8135952Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8136738Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8137583Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8138402Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8139199Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8139957Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8140759Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8141599Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8142428Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8143231Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8143998Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8144793Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8145653Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8146487Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8147303Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8148088Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8148881Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8149727Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8150556Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8151348Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8152113Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8152905Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8153751Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8154587Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8155412Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8156174Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8156967Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8157814Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8158651Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8159457Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8160242Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8161187Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8162076Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8162906Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8163709Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8164478Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8165284Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8166163Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8167013Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8167837Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8168667Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:54:52.8169522Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8170418Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8171297Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:54:52.8172223Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:54:52.8173096Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8173923Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8174825Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8175682Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8176531Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8177311Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8178119Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8178985Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8179828Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8180639Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8181423Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8182266Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8183138Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8183953Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8184745Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8185499Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8186284Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8187137Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8187982Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8188777Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8189584Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8190374Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8191222Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8192046Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8192849Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8193612Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8194403Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8195258Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8196107Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8196906Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8197667Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8198460Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8199310Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8200153Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8200950Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8201734Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8202547Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8203441Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8204293Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8205349Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8206136Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8206977Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8207897Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8208792Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8209683Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8210518Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8211386Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8212400Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8213304Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8214171Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8219942Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8220925Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8221849Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8222741Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8223571Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8224355Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8225161Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8226305Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8227195Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8228029Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8228827Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8229679Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8230552Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8231396Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8232216Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8233013Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8233850Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8234718Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8235584Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8236678Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8237516Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8238338Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8239217Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8240069Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8240893Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8241673Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8242469Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8243326Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8244162Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8244974Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8245992Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8246853Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8247734Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8248609Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8249439Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8250283Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8251105Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8252129Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8253039Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8253887Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8254690Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8255537Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8256722Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8257601Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8258445Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8259244Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8260066Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8261077Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8261933Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8262753Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8263598Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8264444Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8265319Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8266477Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8267328Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8268122Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8268946Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8269826Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8270681Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8271500Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8272282Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8273113Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8273994Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8274836Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8275884Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8276750Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8277592Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8278473Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8279336Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8280162Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8280968Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8281784Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8282643Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8283487Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8284302Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8285084Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8286515Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8287455Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8288331Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8289186Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8289993Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8290828Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8291798Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8292700Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8293546Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8294422Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8295276Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8296474Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8297373Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8298234Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8299057Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8299911Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8300808Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8301691Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8302558Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8303392Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8304231Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8305117Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8306220Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8307087Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8307882Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8308730Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8309614Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8310481Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8311313Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8312108Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8312932Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8313816Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8314674Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8315501Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8316559Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8317404Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8318287Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8319142Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8319966Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8320759Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8321596Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8322474Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8323342Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8324182Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8324974Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8325819Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8326727Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8327888Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8328744Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8329554Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8330398Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8331319Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8332277Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8333130Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8333935Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8334781Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8335699Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8336842Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8337728Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8338538Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8339406Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8340301Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8341170Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8342003Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8342765Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8343556Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8344404Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8345248Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8346313Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8347133Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8347958Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8348833Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8349696Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8350531Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8351326Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8352144Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8353043Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8353906Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8354706Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8355474Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8356294Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8357445Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8358317Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8359156Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8359977Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8360929Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8361822Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8362682Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8363512Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8364344Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8365166Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8366330Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8367216Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8368087Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8368878Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8369709Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8370587Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8371529Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8372395Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8373239Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8374101Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8374976Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8376265Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8378758Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8379571Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8380404Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8381656Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8382527Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8383346Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8384154Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8384970Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8386090Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8386972Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8387812Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8388624Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8389458Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8390364Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8391275Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8392103Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8392913Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8393755Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8394652Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8395542Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8396649Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8397495Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8398343Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8399266Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8400146Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8400994Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8401805Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8402644Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8403547Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8404418Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8405298Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8406809Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8407680Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8408592Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8409473Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8410323Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8411163Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8412128Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8413074Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8413984Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8414834Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8415851Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8416753Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8417668Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8418521Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8419344Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8420135Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8420975Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8421851Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8422696Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8423521Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8424318Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8425148Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8426269Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8427164Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8427985Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8428780Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8429606Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8430479Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8431332Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8432157Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8432942Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8433759Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8434649Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8435505Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8436704Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8437511Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8438337Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8439214Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8440091Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8440938Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8441735Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8442589Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8443474Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8444594Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8446360Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8447455Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8448324Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8449251Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8450139Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8451192Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8452096Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8452965Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8453896Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8454778Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8455628Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8456490Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8457333Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8458238Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8459129Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8460339Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8461719Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8462999Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8464403Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8465738Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8467294Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8468541Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8469385Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8470266Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8471432Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8472277Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8473062Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8473907Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8474812Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8475663Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8476510Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8477571Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8478402Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8479277Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8480133Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8480957Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8481745Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8482567Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8483459Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8484318Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8485139Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8486170Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:54:52.8487048Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8488008Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8488904Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:54:52.8489780Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:54:52.8490589Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8491487Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8492468Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8493427Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8494261Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8495061Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8496117Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8497052Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8497946Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8498819Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8499651Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8500522Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8501443Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8502340Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8503236Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8504095Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:54:52.8504963Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8506112Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8507025Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.8507843Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.8508633Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:54:52.8509448Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8510317Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8511160Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.8511986Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.8512788Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:54:52.8513608Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.8514477Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.8515351Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.8516172Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.8516853Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T04:54:52.8517390Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T04:54:52.8518176Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T04:54:52.8518716Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T04:54:52.8519216Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T04:54:52.8519714Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T04:54:52.8520225Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T04:54:52.8520710Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T04:54:52.8521196Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T04:54:52.8521681Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T04:54:52.8522170Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T04:54:52.8522654Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T04:54:52.8523138Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T04:54:52.8523627Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T04:54:52.8524111Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T04:54:52.8524590Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T04:54:52.8525211Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:54:52.8526214Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T04:54:52.8527024Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T04:54:52.8527781Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T04:54:52.8528524Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T04:54:52.8529249Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:54:52.8529949Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:54:52.8530698Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:54:52.8531603Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:54:52.8532426Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:54:52.8533202Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:54:52.8533672Z         
2025-03-14T04:54:52.8534061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8534970Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8535643Z         
2025-03-14T04:54:52.8537751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8539848Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8541696Z         
2025-03-14T04:54:52.8542073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:54:52.8542555Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:54:52.8542817Z         
2025-03-14T04:54:52.8543298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:54:52.8543945Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:54:52.8544293Z         
2025-03-14T04:54:52.8544633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8545430Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8546330Z         
2025-03-14T04:54:52.8546712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8548893Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8550844Z         
2025-03-14T04:54:52.8551217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8551707Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:54:52.8551960Z         
2025-03-14T04:54:52.8552290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8553080Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8553685Z         
2025-03-14T04:54:52.8554026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8556111Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8557987Z         
2025-03-14T04:54:52.8558352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8558825Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:54:52.8559080Z         
2025-03-14T04:54:52.8559411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8560202Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8560953Z         
2025-03-14T04:54:52.8561299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8563394Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8565324Z         
2025-03-14T04:54:52.8565664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8566483Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:54:52.8567107Z         
2025-03-14T04:54:52.8567462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8569673Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8571974Z         
2025-03-14T04:54:52.8572384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8572929Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:54:52.8573222Z         
2025-03-14T04:54:52.8573620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8574110Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:54:52.8574381Z         
2025-03-14T04:54:52.8574711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8575509Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8576150Z         
2025-03-14T04:54:52.8576499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8578628Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8580576Z         
2025-03-14T04:54:52.8580943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8581424Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:54:52.8581686Z         
2025-03-14T04:54:52.8582021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8582827Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8583439Z         
2025-03-14T04:54:52.8583787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8585953Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8587875Z         
2025-03-14T04:54:52.8588249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8588723Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:54:52.8588987Z         
2025-03-14T04:54:52.8589326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8590157Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8590772Z         
2025-03-14T04:54:52.8591143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8593279Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8595234Z         
2025-03-14T04:54:52.8595600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8596105Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:54:52.8596396Z         
2025-03-14T04:54:52.8596779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8597273Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:54:52.8597542Z         
2025-03-14T04:54:52.8597889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8598724Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8599326Z         
2025-03-14T04:54:52.8599711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8601954Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8603913Z         
2025-03-14T04:54:52.8604341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8604853Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:54:52.8605128Z         
2025-03-14T04:54:52.8605497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8606348Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8607006Z         
2025-03-14T04:54:52.8607372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8609628Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8611789Z         
2025-03-14T04:54:52.8612209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8612733Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:54:52.8613007Z         
2025-03-14T04:54:52.8613381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8614257Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8614923Z         
2025-03-14T04:54:52.8615294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8617568Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8619610Z         
2025-03-14T04:54:52.8619994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8620536Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:54:52.8620820Z         
2025-03-14T04:54:52.8621206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8621725Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:54:52.8622009Z         
2025-03-14T04:54:52.8622346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8623185Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8623794Z         
2025-03-14T04:54:52.8624142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8626342Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8628256Z         
2025-03-14T04:54:52.8628624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8629132Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:54:52.8629400Z         
2025-03-14T04:54:52.8629734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8630544Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8631165Z         
2025-03-14T04:54:52.8631512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8633665Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8635614Z         
2025-03-14T04:54:52.8635987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8636491Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:54:52.8636756Z         
2025-03-14T04:54:52.8637094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8637898Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8638514Z         
2025-03-14T04:54:52.8638861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8640967Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8642870Z         
2025-03-14T04:54:52.8643201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8644008Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:54:52.8644626Z         
2025-03-14T04:54:52.8644976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8647274Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8649468Z         
2025-03-14T04:54:52.8649828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8650332Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:54:52.8650602Z         
2025-03-14T04:54:52.8650973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8651569Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:54:52.8651865Z         
2025-03-14T04:54:52.8652210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8653049Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8653655Z         
2025-03-14T04:54:52.8654001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8656158Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8658075Z         
2025-03-14T04:54:52.8658447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8658928Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:54:52.8659189Z         
2025-03-14T04:54:52.8659522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8660330Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8661048Z         
2025-03-14T04:54:52.8661407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8663584Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8665536Z         
2025-03-14T04:54:52.8665904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8666386Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:54:52.8666652Z         
2025-03-14T04:54:52.8666988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8667798Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8668419Z         
2025-03-14T04:54:52.8668764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8670902Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8672804Z         
2025-03-14T04:54:52.8673159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8673647Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:54:52.8673919Z         
2025-03-14T04:54:52.8674317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8674807Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:54:52.8675074Z         
2025-03-14T04:54:52.8675398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8676214Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8676844Z         
2025-03-14T04:54:52.8677196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8679316Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8681270Z         
2025-03-14T04:54:52.8681637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8682131Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:54:52.8682404Z         
2025-03-14T04:54:52.8682756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8683586Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8684198Z         
2025-03-14T04:54:52.8684543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8686802Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8688835Z         
2025-03-14T04:54:52.8689222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8689731Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:54:52.8690035Z         
2025-03-14T04:54:52.8690388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8691244Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8694547Z         
2025-03-14T04:54:52.8695005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8697271Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8699294Z         
2025-03-14T04:54:52.8699680Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8700198Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:54:52.8700487Z         
2025-03-14T04:54:52.8700898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8701477Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:54:52.8701769Z         
2025-03-14T04:54:52.8702124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8703006Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8703646Z         
2025-03-14T04:54:52.8704010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8706265Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8708279Z         
2025-03-14T04:54:52.8708658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8709149Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:54:52.8709417Z         
2025-03-14T04:54:52.8709759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8710613Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8711245Z         
2025-03-14T04:54:52.8711602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8713795Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8715710Z         
2025-03-14T04:54:52.8716080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8716560Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:54:52.8716824Z         
2025-03-14T04:54:52.8717203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8718011Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8718624Z         
2025-03-14T04:54:52.8718974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8721092Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8723017Z         
2025-03-14T04:54:52.8723384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8723873Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:54:52.8724161Z         
2025-03-14T04:54:52.8724528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8725042Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:54:52.8725326Z         
2025-03-14T04:54:52.8725675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8726482Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8727071Z         
2025-03-14T04:54:52.8727421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8729625Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8731730Z         
2025-03-14T04:54:52.8732151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8732676Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:54:52.8732949Z         
2025-03-14T04:54:52.8733293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8734147Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8734790Z         
2025-03-14T04:54:52.8735160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8737425Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8739466Z         
2025-03-14T04:54:52.8739852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8740333Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:54:52.8740583Z         
2025-03-14T04:54:52.8740901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8741670Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8742253Z         
2025-03-14T04:54:52.8742591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8744659Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8746511Z         
2025-03-14T04:54:52.8746833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8747611Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:54:52.8748217Z         
2025-03-14T04:54:52.8748561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8750724Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8752804Z         
2025-03-14T04:54:52.8753161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8753618Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:54:52.8753863Z         
2025-03-14T04:54:52.8754217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8754695Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:54:52.8754955Z         
2025-03-14T04:54:52.8755288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8756080Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8756677Z         
2025-03-14T04:54:52.8757026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8759148Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8761158Z         
2025-03-14T04:54:52.8761531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8762011Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:54:52.8762270Z         
2025-03-14T04:54:52.8762610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8763449Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8764061Z         
2025-03-14T04:54:52.8764407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8766634Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8768572Z         
2025-03-14T04:54:52.8768945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8769424Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:54:52.8769681Z         
2025-03-14T04:54:52.8770017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8770824Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8771519Z         
2025-03-14T04:54:52.8771893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8774235Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8776319Z         
2025-03-14T04:54:52.8776728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8777270Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:54:52.8777566Z         
2025-03-14T04:54:52.8777976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8778535Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:54:52.8778829Z         
2025-03-14T04:54:52.8779199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8780098Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8780710Z         
2025-03-14T04:54:52.8781083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8783279Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8785201Z         
2025-03-14T04:54:52.8785569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8786050Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:54:52.8786305Z         
2025-03-14T04:54:52.8786640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8787461Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8788071Z         
2025-03-14T04:54:52.8788417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8790532Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8792431Z         
2025-03-14T04:54:52.8792829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8793297Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:54:52.8793566Z         
2025-03-14T04:54:52.8793895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8794687Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8795310Z         
2025-03-14T04:54:52.8795660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8797772Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8799680Z         
2025-03-14T04:54:52.8800041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8800515Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:54:52.8800776Z         
2025-03-14T04:54:52.8801141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8801638Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:54:52.8801901Z         
2025-03-14T04:54:52.8802239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8803033Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8803631Z         
2025-03-14T04:54:52.8803995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8806249Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8808274Z         
2025-03-14T04:54:52.8808659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8809182Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:54:52.8809449Z         
2025-03-14T04:54:52.8809800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8810640Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8811291Z         
2025-03-14T04:54:52.8811750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8814080Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8816098Z         
2025-03-14T04:54:52.8816515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8817024Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:54:52.8817296Z         
2025-03-14T04:54:52.8817651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8818504Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8819153Z         
2025-03-14T04:54:52.8819522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8822324Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8824561Z         
2025-03-14T04:54:52.8825002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8825549Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:54:52.8825857Z         
2025-03-14T04:54:52.8826276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8826856Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:54:52.8827155Z         
2025-03-14T04:54:52.8827534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8828446Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8829139Z         
2025-03-14T04:54:52.8829538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8831832Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8833775Z         
2025-03-14T04:54:52.8834142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8834637Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:54:52.8834906Z         
2025-03-14T04:54:52.8835257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8836087Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8836723Z         
2025-03-14T04:54:52.8837090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8839256Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8841205Z         
2025-03-14T04:54:52.8841570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8842047Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:54:52.8842300Z         
2025-03-14T04:54:52.8842629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8843434Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8844045Z         
2025-03-14T04:54:52.8844383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8846640Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8848630Z         
2025-03-14T04:54:52.8849006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8849503Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:54:52.8849777Z         
2025-03-14T04:54:52.8850159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8850678Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:54:52.8850964Z         
2025-03-14T04:54:52.8851347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8852351Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8853069Z         
2025-03-14T04:54:52.8853472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8855821Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8857938Z         
2025-03-14T04:54:52.8858323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8858817Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:54:52.8859087Z         
2025-03-14T04:54:52.8859427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8860272Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8861059Z         
2025-03-14T04:54:52.8861478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8863713Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8865631Z         
2025-03-14T04:54:52.8865997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8866496Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:54:52.8866751Z         
2025-03-14T04:54:52.8867082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8867902Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8868495Z         
2025-03-14T04:54:52.8868868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8870989Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8872905Z         
2025-03-14T04:54:52.8873264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8873743Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:54:52.8874014Z         
2025-03-14T04:54:52.8874381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8874860Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:54:52.8875119Z         
2025-03-14T04:54:52.8875471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8876254Z         x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8876848Z         
2025-03-14T04:54:52.8877195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8879324Z         x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8881234Z         
2025-03-14T04:54:52.8881600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8882065Z         out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:54:52.8882317Z         
2025-03-14T04:54:52.8882666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8883468Z         x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8884084Z         
2025-03-14T04:54:52.8884431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8886557Z         x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8888461Z         
2025-03-14T04:54:52.8888826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8889322Z         out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:54:52.8889575Z         
2025-03-14T04:54:52.8889918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8890756Z         x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8891392Z         
2025-03-14T04:54:52.8891839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8894201Z         x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8896231Z         
2025-03-14T04:54:52.8896610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8897145Z         x_93 += out_51;  out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:54:52.8897419Z         
2025-03-14T04:54:52.8897807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8898313Z         out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:54:52.8898589Z         
2025-03-14T04:54:52.8898940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8899766Z         x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8900393Z         
2025-03-14T04:54:52.8900759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8903017Z         x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8905029Z         
2025-03-14T04:54:52.8905419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8905917Z         out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:54:52.8906188Z         
2025-03-14T04:54:52.8906539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8907384Z         x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8907995Z         
2025-03-14T04:54:52.8908361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8910622Z         x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8912601Z         
2025-03-14T04:54:52.8912999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8913531Z         out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:54:52.8913803Z         
2025-03-14T04:54:52.8914171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8914998Z         x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8915636Z         
2025-03-14T04:54:52.8915991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8918154Z         x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8920048Z         
2025-03-14T04:54:52.8920334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8920480Z         x_99 += out_55;  out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:54:52.8920552Z         
2025-03-14T04:54:52.8920836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8920986Z         out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:54:52.8921049Z         
2025-03-14T04:54:52.8921303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8921804Z         x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8921889Z         
2025-03-14T04:54:52.8922150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8923957Z         x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8924046Z         
2025-03-14T04:54:52.8924329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8924473Z         out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:54:52.8924534Z         
2025-03-14T04:54:52.8924789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8925271Z         x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8925342Z         
2025-03-14T04:54:52.8925602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8927522Z         x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8927599Z         
2025-03-14T04:54:52.8927908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8928059Z         out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:54:52.8928123Z         
2025-03-14T04:54:52.8928412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8928933Z         x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8929025Z         
2025-03-14T04:54:52.8929307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8931259Z         x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8931335Z         
2025-03-14T04:54:52.8931723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8931899Z         x_105 += out_59;  out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:54:52.8931977Z         
2025-03-14T04:54:52.8932295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8932465Z         out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:54:52.8932529Z         
2025-03-14T04:54:52.8932803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8934067Z         x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8934147Z         
2025-03-14T04:54:52.8934444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8936389Z         x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8936466Z         
2025-03-14T04:54:52.8936771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8936950Z         out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:54:52.8937015Z         
2025-03-14T04:54:52.8937286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8937823Z         x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8937895Z         
2025-03-14T04:54:52.8938172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8940088Z         x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8940162Z         
2025-03-14T04:54:52.8940461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8940608Z         out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:54:52.8940671Z         
2025-03-14T04:54:52.8940959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8941480Z         x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8941552Z         
2025-03-14T04:54:52.8941835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8943686Z         x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8943773Z         
2025-03-14T04:54:52.8944054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8944215Z         x_111 += out_63;  out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:54:52.8944295Z         
2025-03-14T04:54:52.8944583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8944723Z         out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:54:52.8944793Z         
2025-03-14T04:54:52.8945041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8945533Z         x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8945603Z         
2025-03-14T04:54:52.8945865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8947707Z         x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8947778Z         
2025-03-14T04:54:52.8948069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8948211Z         out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:54:52.8948272Z         
2025-03-14T04:54:52.8948533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8949024Z         x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8949092Z         
2025-03-14T04:54:52.8949359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8951204Z         x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8951305Z         
2025-03-14T04:54:52.8951595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8951737Z         out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:54:52.8951799Z         
2025-03-14T04:54:52.8952059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8952554Z         x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8952624Z         
2025-03-14T04:54:52.8952888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8954731Z         x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8954806Z         
2025-03-14T04:54:52.8955088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8955244Z         x_117 += out_67;  out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:54:52.8955305Z         
2025-03-14T04:54:52.8955597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8955734Z         out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:54:52.8955805Z         
2025-03-14T04:54:52.8956054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8956574Z         x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8956651Z         
2025-03-14T04:54:52.8956944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8958770Z         x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8958850Z         
2025-03-14T04:54:52.8959139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8959274Z         out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:54:52.8959342Z         
2025-03-14T04:54:52.8959586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8960194Z         x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8960264Z         
2025-03-14T04:54:52.8960657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8962533Z         x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8962608Z         
2025-03-14T04:54:52.8962901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8963043Z         out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:54:52.8963105Z         
2025-03-14T04:54:52.8963410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8963905Z         x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8963998Z         
2025-03-14T04:54:52.8964269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8966101Z         x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8966193Z         
2025-03-14T04:54:52.8966474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.8966632Z         x_123 += out_71;  out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:54:52.8966697Z         
2025-03-14T04:54:52.8966982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8967121Z         out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:54:52.8967192Z         
2025-03-14T04:54:52.8967454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8967989Z         x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.8968055Z         
2025-03-14T04:54:52.8968340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8970276Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8970344Z         
2025-03-14T04:54:52.8970649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8970805Z         out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:54:52.8970878Z         
2025-03-14T04:54:52.8971173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8971840Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.8971943Z         
2025-03-14T04:54:52.8972238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.8974194Z         x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.8996736Z         
2025-03-14T04:54:52.8997325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.8997495Z         out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:54:52.8997580Z         
2025-03-14T04:54:52.8997997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.8998547Z         x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.8998620Z         
2025-03-14T04:54:52.8998906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9000808Z         x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9000912Z         
2025-03-14T04:54:52.9001210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9001377Z         x_129 += out_75;  out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:54:52.9001469Z         
2025-03-14T04:54:52.9001777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9001926Z         out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:54:52.9002002Z         
2025-03-14T04:54:52.9002262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9002787Z         x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9002852Z         
2025-03-14T04:54:52.9003138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9005096Z         x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9005168Z         
2025-03-14T04:54:52.9005493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9005642Z         out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:54:52.9005715Z         
2025-03-14T04:54:52.9005982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9006521Z         x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9006592Z         
2025-03-14T04:54:52.9006878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9008848Z         x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9008956Z         
2025-03-14T04:54:52.9009276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9009434Z         out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:54:52.9009504Z         
2025-03-14T04:54:52.9009788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9010345Z         x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9010419Z         
2025-03-14T04:54:52.9010712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9012939Z         x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9013028Z         
2025-03-14T04:54:52.9013343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9013520Z         x_135 += out_79;  out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:54:52.9013591Z         
2025-03-14T04:54:52.9013912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9014071Z         out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:54:52.9014152Z         
2025-03-14T04:54:52.9014432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9015006Z         x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9015092Z         
2025-03-14T04:54:52.9015396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9017416Z         x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9017504Z         
2025-03-14T04:54:52.9017812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9017954Z         out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:54:52.9018031Z         
2025-03-14T04:54:52.9018293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9018823Z         x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9018898Z         
2025-03-14T04:54:52.9019174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9021091Z         x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9021161Z         
2025-03-14T04:54:52.9021477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9021627Z         out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:54:52.9021690Z         
2025-03-14T04:54:52.9021979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9022502Z         x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9022603Z         
2025-03-14T04:54:52.9022870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9024722Z         x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9024811Z         
2025-03-14T04:54:52.9025108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9025271Z         x_141 += out_83;  out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:54:52.9025336Z         
2025-03-14T04:54:52.9025641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9025789Z         out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:54:52.9025862Z         
2025-03-14T04:54:52.9026121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9026650Z         x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9026716Z         
2025-03-14T04:54:52.9026990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9028848Z         x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9028916Z         
2025-03-14T04:54:52.9029217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9029369Z         out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:54:52.9029440Z         
2025-03-14T04:54:52.9029688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9030185Z         x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9030262Z         
2025-03-14T04:54:52.9030539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9032380Z         x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9032447Z         
2025-03-14T04:54:52.9032756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9032894Z         out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:54:52.9032966Z         
2025-03-14T04:54:52.9033249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9033783Z         x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9033856Z         
2025-03-14T04:54:52.9034131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9035991Z         x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9036076Z         
2025-03-14T04:54:52.9036351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9036506Z         x_147 += out_87;  out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:54:52.9036567Z         
2025-03-14T04:54:52.9036884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9037022Z         out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:54:52.9037092Z         
2025-03-14T04:54:52.9037339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9037831Z         x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9037892Z         
2025-03-14T04:54:52.9038172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9040073Z         x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9040146Z         
2025-03-14T04:54:52.9040497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9040640Z         out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:54:52.9040710Z         
2025-03-14T04:54:52.9040971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9041499Z         x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9041563Z         
2025-03-14T04:54:52.9041853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9043788Z         x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9043887Z         
2025-03-14T04:54:52.9044198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9044337Z         out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:54:52.9044411Z         
2025-03-14T04:54:52.9044739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9045271Z         x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9045339Z         
2025-03-14T04:54:52.9045625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9047572Z         x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9047642Z         
2025-03-14T04:54:52.9047951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9048106Z         x_153 += out_91;  out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:54:52.9048182Z         
2025-03-14T04:54:52.9048494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9048662Z         out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:54:52.9048735Z         
2025-03-14T04:54:52.9049027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9049600Z         x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9049671Z         
2025-03-14T04:54:52.9049993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9052077Z         x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9052182Z         
2025-03-14T04:54:52.9052512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9052672Z         out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:54:52.9052746Z         
2025-03-14T04:54:52.9053007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9053536Z         x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9053603Z         
2025-03-14T04:54:52.9053887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9055830Z         x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9055908Z         
2025-03-14T04:54:52.9056201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9056332Z         out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:54:52.9056400Z         
2025-03-14T04:54:52.9056660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9057156Z         x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9057239Z         
2025-03-14T04:54:52.9057504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9059322Z         x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9059407Z         
2025-03-14T04:54:52.9059684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9059839Z         x_159 += out_95;  out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:54:52.9059899Z         
2025-03-14T04:54:52.9060187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9060329Z         out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:54:52.9060391Z         
2025-03-14T04:54:52.9060872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9061417Z         x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9061491Z         
2025-03-14T04:54:52.9061755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9063612Z         x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9063685Z         
2025-03-14T04:54:52.9063974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9064194Z         out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:54:52.9064257Z         
2025-03-14T04:54:52.9064512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9065003Z         x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9065100Z         
2025-03-14T04:54:52.9065362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9067168Z         x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9067240Z         
2025-03-14T04:54:52.9067519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9067663Z         out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:54:52.9067724Z         
2025-03-14T04:54:52.9067998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9068501Z         x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9068573Z         
2025-03-14T04:54:52.9068840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9070688Z         x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9070779Z         
2025-03-14T04:54:52.9071057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9071220Z         x_165 += out_99;  out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:54:52.9071286Z         
2025-03-14T04:54:52.9071593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9071737Z         out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:54:52.9071807Z         
2025-03-14T04:54:52.9072052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9072563Z         x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9072637Z         
2025-03-14T04:54:52.9072917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9074831Z         x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9074909Z         
2025-03-14T04:54:52.9075209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9075361Z         out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:54:52.9075424Z         
2025-03-14T04:54:52.9075695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9076221Z         x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9076293Z         
2025-03-14T04:54:52.9076573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9078536Z         x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9078639Z         
2025-03-14T04:54:52.9078937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9079085Z         out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:54:52.9079148Z         
2025-03-14T04:54:52.9079415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9079933Z         x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9080008Z         
2025-03-14T04:54:52.9080281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9082199Z         x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9082274Z         
2025-03-14T04:54:52.9082571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9082742Z         x_171 += out_103;  out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:54:52.9082806Z         
2025-03-14T04:54:52.9083108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9083258Z         out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:54:52.9083330Z         
2025-03-14T04:54:52.9083593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9084127Z         x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9084191Z         
2025-03-14T04:54:52.9084504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9086365Z         x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9086448Z         
2025-03-14T04:54:52.9086764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9086910Z         out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:54:52.9086989Z         
2025-03-14T04:54:52.9087265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9087821Z         x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9087897Z         
2025-03-14T04:54:52.9088190Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9090230Z         x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9090312Z         
2025-03-14T04:54:52.9090630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9090788Z         out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:54:52.9090856Z         
2025-03-14T04:54:52.9091171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9091804Z         x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9091913Z         
2025-03-14T04:54:52.9092217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9094109Z         x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9094196Z         
2025-03-14T04:54:52.9094477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9094644Z         x_177 += out_107;  out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:54:52.9094706Z         
2025-03-14T04:54:52.9094994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9095135Z         out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:54:52.9095209Z         
2025-03-14T04:54:52.9095456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9095979Z         x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9096048Z         
2025-03-14T04:54:52.9096333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9098282Z         x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9098351Z         
2025-03-14T04:54:52.9098669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9098827Z         out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:54:52.9098900Z         
2025-03-14T04:54:52.9099160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9099687Z         x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9099767Z         
2025-03-14T04:54:52.9100051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9101878Z         x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9101942Z         
2025-03-14T04:54:52.9102231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9102362Z         out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:54:52.9102432Z         
2025-03-14T04:54:52.9102697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9103226Z         x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9103298Z         
2025-03-14T04:54:52.9103572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9105500Z         x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9105589Z         
2025-03-14T04:54:52.9105882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9106051Z         x_183 += out_111;  out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:54:52.9106114Z         
2025-03-14T04:54:52.9106431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9106578Z         out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:54:52.9106651Z         
2025-03-14T04:54:52.9106908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9107422Z         x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9107487Z         
2025-03-14T04:54:52.9107767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9109690Z         x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9109765Z         
2025-03-14T04:54:52.9110069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9110209Z         out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:54:52.9110280Z         
2025-03-14T04:54:52.9110536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9111060Z         x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9111124Z         
2025-03-14T04:54:52.9111411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9113329Z         x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9113423Z         
2025-03-14T04:54:52.9113726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9113865Z         out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:54:52.9113939Z         
2025-03-14T04:54:52.9114201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9114728Z         x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9114795Z         
2025-03-14T04:54:52.9115076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9117535Z         x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9117614Z         
2025-03-14T04:54:52.9117905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9118056Z         x_189 += out_115;  out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:54:52.9118128Z         
2025-03-14T04:54:52.9118412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9118561Z         out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:54:52.9118634Z         
2025-03-14T04:54:52.9118881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9119391Z         x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9119454Z         
2025-03-14T04:54:52.9119882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9122701Z         x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9122830Z         
2025-03-14T04:54:52.9123139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9123275Z         out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191);  x_191 = None
2025-03-14T04:54:52.9123345Z         
2025-03-14T04:54:52.9123605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9124110Z         x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9124193Z         
2025-03-14T04:54:52.9124632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9126752Z         x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9126823Z         
2025-03-14T04:54:52.9127133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9127275Z         out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193);  x_193 = None
2025-03-14T04:54:52.9127360Z         
2025-03-14T04:54:52.9127802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9128645Z         x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9128770Z         
2025-03-14T04:54:52.9129227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9131930Z         x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9132082Z         
2025-03-14T04:54:52.9132562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9133366Z         x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:54:52.9133450Z         
2025-03-14T04:54:52.9133823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9136644Z         x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9136733Z         
2025-03-14T04:54:52.9137076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9137251Z         x_195 += x_197;  out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195;  x_195 = x_197 = None
2025-03-14T04:54:52.9137338Z         
2025-03-14T04:54:52.9137661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9137860Z         out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122);  out_122 = None
2025-03-14T04:54:52.9137933Z         
2025-03-14T04:54:52.9138223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9138795Z         x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9138873Z         
2025-03-14T04:54:52.9139171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9141276Z         x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9141357Z         
2025-03-14T04:54:52.9141692Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9141864Z         out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199);  x_199 = None
2025-03-14T04:54:52.9141934Z         
2025-03-14T04:54:52.9148981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9149560Z         x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9149650Z         
2025-03-14T04:54:52.9149950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9151763Z         x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9151844Z         
2025-03-14T04:54:52.9152163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9152323Z         out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201);  x_201 = None
2025-03-14T04:54:52.9152450Z         
2025-03-14T04:54:52.9152731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9153254Z         x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9153346Z         
2025-03-14T04:54:52.9153618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9155526Z         x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9155608Z         
2025-03-14T04:54:52.9155929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9156092Z         x_203 += out_123;  out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203;  x_203 = out_123 = None
2025-03-14T04:54:52.9156162Z         
2025-03-14T04:54:52.9156447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9156622Z         out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126);  out_126 = None
2025-03-14T04:54:52.9156689Z         
2025-03-14T04:54:52.9156958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9157452Z         x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9157531Z         
2025-03-14T04:54:52.9157814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9159749Z         x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9159859Z         
2025-03-14T04:54:52.9160168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9160347Z         out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205);  x_205 = None
2025-03-14T04:54:52.9160413Z         
2025-03-14T04:54:52.9160824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9161327Z         x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9161403Z         
2025-03-14T04:54:52.9161668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9163471Z         x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9163551Z         
2025-03-14T04:54:52.9163887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9164042Z         out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207);  x_207 = None
2025-03-14T04:54:52.9164110Z         
2025-03-14T04:54:52.9164397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9164934Z         x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9165007Z         
2025-03-14T04:54:52.9165304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9167353Z         x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9167487Z         
2025-03-14T04:54:52.9167814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9167997Z         x_209 += out_127;  out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209;  x_209 = out_127 = None
2025-03-14T04:54:52.9168079Z         
2025-03-14T04:54:52.9168399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9168574Z         out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130);  out_130 = None
2025-03-14T04:54:52.9168646Z         
2025-03-14T04:54:52.9168985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9169692Z         x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T04:54:52.9169838Z         
2025-03-14T04:54:52.9170224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9182073Z         x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T04:54:52.9182210Z         
2025-03-14T04:54:52.9182682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:54:52.9182975Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest');  x_210 = None
2025-03-14T04:54:52.9183043Z         
2025-03-14T04:54:52.9183316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9183917Z         x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T04:54:52.9183988Z         
2025-03-14T04:54:52.9184345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:54:52.9184550Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features;  x_212 = top_down_features = None
2025-03-14T04:54:52.9184613Z         
2025-03-14T04:54:52.9205566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9206216Z         x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T04:54:52.9206308Z         
2025-03-14T04:54:52.9206736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:54:52.9207086Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T04:54:52.9207157Z         
2025-03-14T04:54:52.9207414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9208023Z         x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T04:54:52.9208084Z         
2025-03-14T04:54:52.9208443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:54:52.9208658Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1;  x_214 = top_down_features_1 = None
2025-03-14T04:54:52.9208725Z         
2025-03-14T04:54:52.9208978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9209592Z         x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T04:54:52.9209658Z         
2025-03-14T04:54:52.9210085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:54:52.9210426Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T04:54:52.9210486Z         
2025-03-14T04:54:52.9210745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9211346Z         x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T04:54:52.9211417Z         
2025-03-14T04:54:52.9211857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:54:52.9212104Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2;  x_216 = top_down_features_2 = None
2025-03-14T04:54:52.9212168Z         
2025-03-14T04:54:52.9212438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9213107Z         x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T04:54:52.9213191Z         
2025-03-14T04:54:52.9213564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T04:54:52.9213781Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T04:54:52.9213846Z         
2025-03-14T04:54:52.9214303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:52.9214468Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T04:54:52.9214532Z         
2025-03-14T04:54:52.9214844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9214989Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:54:52.9215053Z         
2025-03-14T04:54:52.9215515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:52.9215676Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T04:54:52.9215740Z         
2025-03-14T04:54:52.9216051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9216194Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:54:52.9216260Z         
2025-03-14T04:54:52.9216668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:54:52.9216863Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:54:52.9216964Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T04:54:52.9217095Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:54:52.9217158Z         
2025-03-14T04:54:52.9217511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:54:52.9217645Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:54:52.9217718Z         
2025-03-14T04:54:52.9218058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:54:52.9218193Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:54:52.9218257Z         
2025-03-14T04:54:52.9218713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:54:52.9218934Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:54:52.9219016Z         
2025-03-14T04:54:52.9219461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:54:52.9219594Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:54:52.9220042Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:54:52.9220184Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:54:52.9220311Z         x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T04:54:52.9220373Z         
2025-03-14T04:54:52.9220833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:52.9220986Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T04:54:52.9221052Z         
2025-03-14T04:54:52.9221358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9221508Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:54:52.9221570Z         
2025-03-14T04:54:52.9222029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:52.9222178Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T04:54:52.9222249Z         
2025-03-14T04:54:52.9222551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9222699Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T04:54:52.9222761Z         
2025-03-14T04:54:52.9223171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:54:52.9223365Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T04:54:52.9223470Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T04:54:52.9223593Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T04:54:52.9223658Z         
2025-03-14T04:54:52.9223979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:54:52.9224108Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T04:54:52.9224170Z         
2025-03-14T04:54:52.9224500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:54:52.9224625Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T04:54:52.9224692Z         
2025-03-14T04:54:52.9225093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:54:52.9225316Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T04:54:52.9225393Z         
2025-03-14T04:54:52.9225819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:54:52.9225953Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T04:54:52.9226407Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T04:54:52.9226538Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T04:54:52.9226652Z         x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T04:54:52.9226721Z         
2025-03-14T04:54:52.9227164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:52.9227315Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T04:54:52.9227378Z         
2025-03-14T04:54:52.9227685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9227824Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T04:54:52.9227890Z         
2025-03-14T04:54:52.9228329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:52.9228486Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T04:54:52.9228546Z         
2025-03-14T04:54:52.9228841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9228973Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T04:54:52.9229036Z         
2025-03-14T04:54:52.9229417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:54:52.9229614Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T04:54:52.9229710Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T04:54:52.9229830Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T04:54:52.9229890Z         
2025-03-14T04:54:52.9230215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:54:52.9230335Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T04:54:52.9230400Z         
2025-03-14T04:54:52.9230721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:54:52.9230846Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T04:54:52.9230904Z         
2025-03-14T04:54:52.9231304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:54:52.9231514Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T04:54:52.9231595Z         
2025-03-14T04:54:52.9232000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:54:52.9232132Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T04:54:52.9232569Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T04:54:52.9232689Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T04:54:52.9232804Z         x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T04:54:52.9232865Z         
2025-03-14T04:54:52.9233306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:52.9233448Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T04:54:52.9233511Z         
2025-03-14T04:54:52.9233802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9233941Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T04:54:52.9234000Z         
2025-03-14T04:54:52.9234434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:52.9234574Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T04:54:52.9234639Z         
2025-03-14T04:54:52.9234924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9235061Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T04:54:52.9235138Z         
2025-03-14T04:54:52.9235511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:54:52.9235698Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T04:54:52.9235799Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T04:54:52.9235911Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T04:54:52.9235979Z         
2025-03-14T04:54:52.9236302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:54:52.9236426Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T04:54:52.9236487Z         
2025-03-14T04:54:52.9236813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:54:52.9236928Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T04:54:52.9236993Z         
2025-03-14T04:54:52.9237401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:54:52.9237626Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T04:54:52.9237685Z         
2025-03-14T04:54:52.9238096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:54:52.9238236Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T04:54:52.9238660Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T04:54:52.9238782Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T04:54:52.9238891Z         x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T04:54:52.9238956Z         
2025-03-14T04:54:52.9239382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:52.9239521Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:54:52.9239580Z         
2025-03-14T04:54:52.9239870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9240009Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T04:54:52.9240070Z         
2025-03-14T04:54:52.9240501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:52.9240642Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:54:52.9240710Z         
2025-03-14T04:54:52.9240996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9241150Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T04:54:52.9241210Z         
2025-03-14T04:54:52.9241597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:54:52.9241782Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T04:54:52.9241884Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T04:54:52.9242000Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T04:54:52.9242070Z         
2025-03-14T04:54:52.9242398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:54:52.9242520Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T04:54:52.9242583Z         
2025-03-14T04:54:52.9242910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:54:52.9243026Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T04:54:52.9243106Z         
2025-03-14T04:54:52.9243493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:54:52.9243711Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T04:54:52.9243778Z         
2025-03-14T04:54:52.9244182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:54:52.9244328Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T04:54:52.9244746Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T04:54:52.9244866Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T04:54:52.9244975Z         x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T04:54:52.9245039Z         
2025-03-14T04:54:52.9245339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:54:52.9245472Z         tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32);  x_218 = None
2025-03-14T04:54:52.9245603Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32);  x_219 = None
2025-03-14T04:54:52.9245732Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32);  x_220 = None
2025-03-14T04:54:52.9245851Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32);  x_221 = None
2025-03-14T04:54:52.9245978Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32);  x_222 = None
2025-03-14T04:54:52.9246038Z         
2025-03-14T04:54:52.9246305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9246839Z         x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_217 = None
2025-03-14T04:54:52.9246907Z         
2025-03-14T04:54:52.9247202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:54:52.9247408Z         x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False);  x_223 = None
2025-03-14T04:54:52.9247471Z         
2025-03-14T04:54:52.9247869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:54:52.9248405Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:54:52.9248467Z         
2025-03-14T04:54:52.9248847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:54:52.9249413Z         x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_224 = None
2025-03-14T04:54:52.9249482Z         
2025-03-14T04:54:52.9249751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9250285Z         x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_215 = None
2025-03-14T04:54:52.9250347Z         
2025-03-14T04:54:52.9250657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:54:52.9250858Z         x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False);  x_225 = None
2025-03-14T04:54:52.9250928Z         
2025-03-14T04:54:52.9251320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:54:52.9252010Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:54:52.9252106Z         
2025-03-14T04:54:52.9252507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:54:52.9253089Z         x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_226 = None
2025-03-14T04:54:52.9253152Z         
2025-03-14T04:54:52.9253415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9253902Z         x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_213 = None
2025-03-14T04:54:52.9253992Z         
2025-03-14T04:54:52.9254271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:54:52.9254465Z         x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False);  x_227 = None
2025-03-14T04:54:52.9254525Z         
2025-03-14T04:54:52.9254908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:54:52.9255425Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:54:52.9255494Z         
2025-03-14T04:54:52.9255854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:54:52.9256396Z         x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_228 = None
2025-03-14T04:54:52.9256464Z         
2025-03-14T04:54:52.9256734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9257220Z         x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_211 = None
2025-03-14T04:54:52.9257299Z         
2025-03-14T04:54:52.9257580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:54:52.9257765Z         x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False);  x_229 = None
2025-03-14T04:54:52.9257831Z         
2025-03-14T04:54:52.9258209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:54:52.9258727Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:54:52.9258789Z         
2025-03-14T04:54:52.9259156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:54:52.9259675Z         x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_230 = None
2025-03-14T04:54:52.9259741Z         
2025-03-14T04:54:52.9259999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9261012Z         x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:54:52.9261086Z         
2025-03-14T04:54:52.9261362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:54:52.9261538Z         x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False);  x_231 = None
2025-03-14T04:54:52.9261600Z         
2025-03-14T04:54:52.9261974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:54:52.9262832Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:54:52.9262922Z         
2025-03-14T04:54:52.9263279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:54:52.9264140Z         x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:54:52.9264860Z         
2025-03-14T04:54:52.9265200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:54:52.9265371Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:54:52.9265513Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:54:52.9265680Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T04:54:52.9265819Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T04:54:52.9265974Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T04:54:52.9266110Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T04:54:52.9266262Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T04:54:52.9266391Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T04:54:52.9266535Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T04:54:52.9266665Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T04:54:52.9266733Z         
2025-03-14T04:54:52.9267162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:54:52.9267340Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304);  x_233 = None
2025-03-14T04:54:52.9267540Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T04:54:52.9267721Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T04:54:52.9267887Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152);  x_234 = None
2025-03-14T04:54:52.9268055Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T04:54:52.9268229Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T04:54:52.9268376Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76);  x_235 = None
2025-03-14T04:54:52.9268550Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T04:54:52.9268721Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T04:54:52.9268869Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38);  x_236 = None
2025-03-14T04:54:52.9269042Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T04:54:52.9269218Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T04:54:52.9269371Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19);  x_237 = None
2025-03-14T04:54:52.9269532Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T04:54:52.9269693Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T04:54:52.9269783Z         
2025-03-14T04:54:52.9270198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:54:52.9270415Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:54:52.9270473Z         
2025-03-14T04:54:52.9270928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:54:52.9271080Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:54:52.9271229Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:54:52.9271366Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:54:52.9271433Z         
2025-03-14T04:54:52.9271807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:54:52.9271979Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:54:52.9272036Z         
2025-03-14T04:54:52.9272349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:54:52.9272495Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:54:52.9272554Z         
2025-03-14T04:54:52.9272870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:54:52.9273017Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:54:52.9273146Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:54:52.9273295Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:54:52.9273359Z         
2025-03-14T04:54:52.9273674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:54:52.9273804Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:54:52.9273923Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:54:52.9274073Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T04:54:52.9274131Z         
2025-03-14T04:54:52.9274442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:54:52.9274562Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:54:52.9274649Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T04:54:52.9274806Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T04:54:52.9274872Z         
2025-03-14T04:54:52.9275178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:54:52.9275343Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:54:52.9275429Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T04:54:52.9275561Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T04:54:52.9275618Z         
2025-03-14T04:54:52.9275980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:54:52.9276137Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:54:52.9276256Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T04:54:52.9276315Z         
2025-03-14T04:54:52.9276616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:54:52.9276767Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:54:52.9276880Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T04:54:52.9276940Z         
2025-03-14T04:54:52.9277238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:54:52.9277397Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:54:52.9277512Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T04:54:52.9277572Z         
2025-03-14T04:54:52.9277872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:54:52.9278128Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:54:52.9278241Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T04:54:52.9278300Z         
2025-03-14T04:54:52.9278657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:54:52.9278818Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:54:52.9278927Z         
2025-03-14T04:54:52.9279272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:54:52.9279435Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:54:52.9279492Z         
2025-03-14T04:54:52.9279946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:54:52.9280081Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:54:52.9280201Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T04:54:52.9280405Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:54:52.9280537Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T04:54:52.9280601Z         
2025-03-14T04:54:52.9280961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:54:52.9281257Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:54:52.9281392Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T04:54:52.9281540Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:54:52.9281679Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T04:54:52.9281785Z         
2025-03-14T04:54:52.9282145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:54:52.9282273Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:54:52.9282442Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:54:52.9282567Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T04:54:52.9282632Z         
2025-03-14T04:54:52.9282954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:54:52.9283067Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:54:52.9283227Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:54:52.9283363Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T04:54:52.9283421Z         
2025-03-14T04:54:52.9283727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:54:52.9283820Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:54:52.9283940Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:54:52.9284001Z         
2025-03-14T04:54:52.9284305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:54:52.9284520Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:54:52.9284638Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:54:52.9284758Z         
2025-03-14T04:54:52.9285144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:54:52.9285257Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:54:52.9285384Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:54:52.9285445Z         
2025-03-14T04:54:52.9285738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:54:52.9285850Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:54:52.9285973Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:54:52.9286033Z         
2025-03-14T04:54:52.9286372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:54:52.9286552Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:54:52.9286610Z         
2025-03-14T04:54:52.9286961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:54:52.9287120Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T04:54:52.9287196Z         
2025-03-14T04:54:52.9287573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:54:52.9287747Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:54:52.9287803Z         
2025-03-14T04:54:52.9288228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:54:52.9288436Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T04:54:52.9288500Z         
2025-03-14T04:54:52.9288933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:54:52.9289088Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T04:54:52.9289235Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T04:54:52.9289376Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T04:54:52.9289438Z         
2025-03-14T04:54:52.9289809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:54:52.9289977Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T04:54:52.9290046Z         
2025-03-14T04:54:52.9290350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:54:52.9290497Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T04:54:52.9290556Z         
2025-03-14T04:54:52.9290868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:54:52.9291029Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T04:54:52.9291155Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:54:52.9291311Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T04:54:52.9291372Z         
2025-03-14T04:54:52.9291799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:54:52.9291935Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T04:54:52.9292063Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T04:54:52.9292220Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T04:54:52.9292285Z         
2025-03-14T04:54:52.9292605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:54:52.9292734Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:54:52.9292827Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T04:54:52.9292989Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T04:54:52.9293051Z         
2025-03-14T04:54:52.9293374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:54:52.9293544Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T04:54:52.9293640Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T04:54:52.9293773Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T04:54:52.9293854Z         
2025-03-14T04:54:52.9294173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:54:52.9294340Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:54:52.9294459Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T04:54:52.9294530Z         
2025-03-14T04:54:52.9294843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:54:52.9295010Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:54:52.9295126Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T04:54:52.9295197Z         
2025-03-14T04:54:52.9295507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:54:52.9295671Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:54:52.9295787Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T04:54:52.9295859Z         
2025-03-14T04:54:52.9296172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:54:52.9296376Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T04:54:52.9296489Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T04:54:52.9296560Z         
2025-03-14T04:54:52.9296926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:54:52.9297083Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T04:54:52.9297147Z         
2025-03-14T04:54:52.9297503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:54:52.9297642Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T04:54:52.9297716Z         
2025-03-14T04:54:52.9298078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:54:52.9298230Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T04:54:52.9298360Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T04:54:52.9298536Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T04:54:52.9298682Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T04:54:52.9298755Z         
2025-03-14T04:54:52.9299133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:54:52.9299302Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T04:54:52.9299434Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T04:54:52.9299595Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T04:54:52.9299744Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T04:54:52.9299828Z         
2025-03-14T04:54:52.9300181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:54:52.9300298Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T04:54:52.9300475Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T04:54:52.9300615Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T04:54:52.9300687Z         
2025-03-14T04:54:52.9301036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:54:52.9301159Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T04:54:52.9301334Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T04:54:52.9301483Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T04:54:52.9301548Z         
2025-03-14T04:54:52.9301884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:54:52.9301985Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T04:54:52.9302118Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T04:54:52.9302185Z         
2025-03-14T04:54:52.9302517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:54:52.9302614Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T04:54:52.9302759Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T04:54:52.9302827Z         
2025-03-14T04:54:52.9303147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:54:52.9303272Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T04:54:52.9303416Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T04:54:52.9303480Z         
2025-03-14T04:54:52.9303800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:54:52.9303918Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T04:54:52.9304055Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T04:54:52.9304119Z         
2025-03-14T04:54:52.9304476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:54:52.9304672Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T04:54:52.9304760Z         
2025-03-14T04:54:52.9305101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:54:52.9305306Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T04:54:52.9305368Z         
2025-03-14T04:54:52.9305767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:54:52.9305947Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T04:54:52.9306033Z         
2025-03-14T04:54:52.9306576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:54:52.9306802Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T04:54:52.9306866Z         
2025-03-14T04:54:52.9307321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:54:52.9307483Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T04:54:52.9307635Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T04:54:52.9307783Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T04:54:52.9307846Z         
2025-03-14T04:54:52.9308232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:54:52.9308399Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T04:54:52.9308474Z         
2025-03-14T04:54:52.9308791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:54:52.9308942Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T04:54:52.9309003Z         
2025-03-14T04:54:52.9309343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:54:52.9309476Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T04:54:52.9309607Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:54:52.9309756Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T04:54:52.9309826Z         
2025-03-14T04:54:52.9310147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:54:52.9310278Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T04:54:52.9310397Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T04:54:52.9310554Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T04:54:52.9310620Z         
2025-03-14T04:54:52.9310941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:54:52.9311077Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:54:52.9311179Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T04:54:52.9311308Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T04:54:52.9311395Z         
2025-03-14T04:54:52.9311713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:54:52.9311871Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T04:54:52.9311965Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T04:54:52.9312127Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T04:54:52.9312190Z         
2025-03-14T04:54:52.9312503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:54:52.9312659Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:54:52.9312781Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T04:54:52.9312844Z         
2025-03-14T04:54:52.9313152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:54:52.9313302Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:54:52.9313418Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T04:54:52.9313484Z         
2025-03-14T04:54:52.9313792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:54:52.9313949Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:54:52.9314058Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T04:54:52.9314127Z         
2025-03-14T04:54:52.9314432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:54:52.9314625Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T04:54:52.9314735Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T04:54:52.9314805Z         
2025-03-14T04:54:52.9315188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:54:52.9315343Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T04:54:52.9315404Z         
2025-03-14T04:54:52.9315749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:54:52.9315888Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T04:54:52.9315963Z         
2025-03-14T04:54:52.9316313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:54:52.9316455Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T04:54:52.9316585Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T04:54:52.9316750Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T04:54:52.9316910Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T04:54:52.9316981Z         
2025-03-14T04:54:52.9317329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:54:52.9317500Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T04:54:52.9317621Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T04:54:52.9317779Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T04:54:52.9317940Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T04:54:52.9318013Z         
2025-03-14T04:54:52.9318349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:54:52.9318473Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T04:54:52.9318638Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T04:54:52.9318782Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T04:54:52.9318844Z         
2025-03-14T04:54:52.9319188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:54:52.9319322Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T04:54:52.9319500Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T04:54:52.9319632Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T04:54:52.9319702Z         
2025-03-14T04:54:52.9320016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:54:52.9320124Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T04:54:52.9320249Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T04:54:52.9320317Z         
2025-03-14T04:54:52.9320620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:54:52.9320718Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T04:54:52.9320849Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T04:54:52.9320918Z         
2025-03-14T04:54:52.9321217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:54:52.9321340Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T04:54:52.9321480Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T04:54:52.9321541Z         
2025-03-14T04:54:52.9321846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:54:52.9321958Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T04:54:52.9322090Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T04:54:52.9322155Z         
2025-03-14T04:54:52.9322506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:54:52.9322708Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T04:54:52.9322777Z         
2025-03-14T04:54:52.9323104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:54:52.9323286Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T04:54:52.9323350Z         
2025-03-14T04:54:52.9323733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:54:52.9323921Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T04:54:52.9323993Z         
2025-03-14T04:54:52.9324398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:54:52.9324611Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T04:54:52.9324677Z         
2025-03-14T04:54:52.9325119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:54:52.9325268Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T04:54:52.9325427Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T04:54:52.9325565Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T04:54:52.9325635Z         
2025-03-14T04:54:52.9326011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:54:52.9326186Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T04:54:52.9326252Z         
2025-03-14T04:54:52.9326571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:54:52.9326715Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T04:54:52.9326787Z         
2025-03-14T04:54:52.9327118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:54:52.9327257Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T04:54:52.9327383Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:54:52.9327541Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T04:54:52.9327605Z         
2025-03-14T04:54:52.9327939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:54:52.9328061Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T04:54:52.9328187Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T04:54:52.9328346Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T04:54:52.9328411Z         
2025-03-14T04:54:52.9328740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:54:52.9328896Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:54:52.9328998Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T04:54:52.9329130Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T04:54:52.9329220Z         
2025-03-14T04:54:52.9329544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:54:52.9329700Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T04:54:52.9329814Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T04:54:52.9329953Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T04:54:52.9330019Z         
2025-03-14T04:54:52.9330347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:54:52.9330505Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:54:52.9330628Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T04:54:52.9330695Z         
2025-03-14T04:54:52.9331018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:54:52.9331174Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:54:52.9331297Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T04:54:52.9331363Z         
2025-03-14T04:54:52.9331781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:54:52.9331946Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:54:52.9332067Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T04:54:52.9332134Z         
2025-03-14T04:54:52.9332466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:54:52.9332649Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T04:54:52.9332769Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T04:54:52.9332854Z         
2025-03-14T04:54:52.9333207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:54:52.9333350Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T04:54:52.9333422Z         
2025-03-14T04:54:52.9333757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:54:52.9333906Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T04:54:52.9333968Z         
2025-03-14T04:54:52.9334323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:54:52.9334461Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T04:54:52.9334594Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T04:54:52.9334760Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T04:54:52.9334916Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T04:54:52.9334988Z         
2025-03-14T04:54:52.9335339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:54:52.9335510Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T04:54:52.9335628Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T04:54:52.9335782Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T04:54:52.9335965Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T04:54:52.9336033Z         
2025-03-14T04:54:52.9336359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:54:52.9336476Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T04:54:52.9336633Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T04:54:52.9336774Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T04:54:52.9336834Z         
2025-03-14T04:54:52.9337170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:54:52.9337280Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T04:54:52.9337449Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T04:54:52.9337577Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T04:54:52.9337646Z         
2025-03-14T04:54:52.9337951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:54:52.9338054Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T04:54:52.9338167Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T04:54:52.9338236Z         
2025-03-14T04:54:52.9338541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:54:52.9338658Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T04:54:52.9338772Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T04:54:52.9338840Z         
2025-03-14T04:54:52.9339142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:54:52.9339261Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T04:54:52.9339389Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T04:54:52.9339460Z         
2025-03-14T04:54:52.9339760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:54:52.9339879Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T04:54:52.9340009Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T04:54:52.9340081Z         
2025-03-14T04:54:52.9340424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:54:52.9340628Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T04:54:52.9340690Z         
2025-03-14T04:54:52.9341040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:54:52.9341198Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T04:54:52.9341265Z         
2025-03-14T04:54:52.9341640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:54:52.9341838Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T04:54:52.9341898Z         
2025-03-14T04:54:52.9342301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:54:52.9342508Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T04:54:52.9342570Z         
2025-03-14T04:54:52.9343004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:54:52.9343151Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T04:54:52.9343304Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T04:54:52.9343435Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T04:54:52.9343507Z         
2025-03-14T04:54:52.9343878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:54:52.9344051Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T04:54:52.9344114Z         
2025-03-14T04:54:52.9344427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:54:52.9344583Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T04:54:52.9344655Z         
2025-03-14T04:54:52.9344964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:54:52.9345095Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T04:54:52.9345214Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:54:52.9345366Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T04:54:52.9345429Z         
2025-03-14T04:54:52.9345749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:54:52.9345868Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T04:54:52.9345994Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T04:54:52.9346143Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T04:54:52.9346211Z         
2025-03-14T04:54:52.9346533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:54:52.9346662Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:54:52.9346747Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T04:54:52.9346900Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T04:54:52.9346961Z         
2025-03-14T04:54:52.9347278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:54:52.9347420Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T04:54:52.9347532Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T04:54:52.9347654Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T04:54:52.9347724Z         
2025-03-14T04:54:52.9348026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:54:52.9348183Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:54:52.9348293Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T04:54:52.9348363Z         
2025-03-14T04:54:52.9348661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:54:52.9348814Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:54:52.9348922Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T04:54:52.9348990Z         
2025-03-14T04:54:52.9349298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:54:52.9349441Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:54:52.9349553Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T04:54:52.9349615Z         
2025-03-14T04:54:52.9349924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:54:52.9350102Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T04:54:52.9350241Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T04:54:52.9350306Z         
2025-03-14T04:54:52.9350652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:54:52.9350787Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T04:54:52.9350857Z         
2025-03-14T04:54:52.9351185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:54:52.9351323Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T04:54:52.9351383Z         
2025-03-14T04:54:52.9351731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:54:52.9351865Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T04:54:52.9351989Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T04:54:52.9352163Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T04:54:52.9352303Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T04:54:52.9352366Z         
2025-03-14T04:54:52.9352731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:54:52.9352861Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T04:54:52.9352986Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T04:54:52.9353132Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T04:54:52.9353287Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T04:54:52.9353348Z         
2025-03-14T04:54:52.9353689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:54:52.9353801Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T04:54:52.9353966Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T04:54:52.9354096Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T04:54:52.9354163Z         
2025-03-14T04:54:52.9354494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:54:52.9354614Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T04:54:52.9354774Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T04:54:52.9354907Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T04:54:52.9354968Z         
2025-03-14T04:54:52.9355282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:54:52.9355377Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T04:54:52.9355495Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T04:54:52.9355555Z         
2025-03-14T04:54:52.9355866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:54:52.9355985Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T04:54:52.9356096Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T04:54:52.9356157Z         
2025-03-14T04:54:52.9356468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:54:52.9356586Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T04:54:52.9356716Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T04:54:52.9356784Z         
2025-03-14T04:54:52.9357081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:54:52.9357200Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T04:54:52.9357329Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T04:54:52.9357398Z         
2025-03-14T04:54:52.9357760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:54:52.9357949Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T04:54:52.9358009Z         
2025-03-14T04:54:52.9358358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:54:52.9358513Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T04:54:52.9358580Z         
2025-03-14T04:54:52.9358959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:54:52.9359149Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T04:54:52.9359210Z         
2025-03-14T04:54:52.9359702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:54:52.9359831Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:54:52.9359898Z         
2025-03-14T04:54:52.9360187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9360329Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T04:54:52.9360394Z         
2025-03-14T04:54:52.9361017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:54:52.9361137Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T04:54:52.9361249Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T04:54:52.9361361Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:54:52.9361432Z         
2025-03-14T04:54:52.9361891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:54:52.9362033Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:54:52.9362308Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T04:54:52.9362383Z         
2025-03-14T04:54:52.9362862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:54:52.9363048Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:54:52.9363109Z         
2025-03-14T04:54:52.9363412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9363538Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T04:54:52.9363601Z         
2025-03-14T04:54:52.9364053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:54:52.9364172Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T04:54:52.9364313Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T04:54:52.9364428Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T04:54:52.9364499Z         
2025-03-14T04:54:52.9364951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:54:52.9365110Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:54:52.9365345Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T04:54:52.9365436Z         
2025-03-14T04:54:52.9365892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:54:52.9366060Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:54:52.9366120Z         
2025-03-14T04:54:52.9366420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9366542Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T04:54:52.9366611Z         
2025-03-14T04:54:52.9367056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:54:52.9367178Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T04:54:52.9367295Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T04:54:52.9367418Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T04:54:52.9367480Z         
2025-03-14T04:54:52.9367945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:54:52.9368081Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:54:52.9368327Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T04:54:52.9368390Z         
2025-03-14T04:54:52.9368881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:54:52.9369047Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:54:52.9369118Z         
2025-03-14T04:54:52.9369414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9369546Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T04:54:52.9369609Z         
2025-03-14T04:54:52.9370061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:54:52.9370182Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T04:54:52.9370286Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T04:54:52.9370408Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T04:54:52.9370472Z         
2025-03-14T04:54:52.9370963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:54:52.9371113Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:54:52.9371366Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T04:54:52.9371500Z         
2025-03-14T04:54:52.9372004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:54:52.9372207Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:54:52.9372280Z         
2025-03-14T04:54:52.9372595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9372721Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T04:54:52.9372780Z         
2025-03-14T04:54:52.9373204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:54:52.9373309Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T04:54:52.9373417Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T04:54:52.9373528Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T04:54:52.9373599Z         
2025-03-14T04:54:52.9374051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:54:52.9374220Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:54:52.9374446Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T04:54:52.9374517Z         
2025-03-14T04:54:52.9374983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:54:52.9375149Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:54:52.9375210Z         
2025-03-14T04:54:52.9375505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:52.9375623Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T04:54:52.9375692Z         
2025-03-14T04:54:52.9375966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:54:52.9376345Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T04:54:52.9376414Z         
2025-03-14T04:54:52.9376689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:54:52.9377174Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T04:54:52.9377250Z         
2025-03-14T04:54:52.9377535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:54:52.9377737Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T04:54:52.9377807Z         
2025-03-14T04:54:52.9378201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:54:52.9378346Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T04:54:52.9378408Z         
2025-03-14T04:54:52.9378710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:54:52.9378855Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T04:54:52.9378924Z         
2025-03-14T04:54:52.9379294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:54:52.9379432Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T04:54:52.9379496Z         
2025-03-14T04:54:52.9379980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:54:52.9380115Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T04:54:52.9380238Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:54:52.9380397Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:54:52.9380534Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:54:52.9380595Z         
2025-03-14T04:54:52.9380967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:54:52.9381096Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:54:52.9381167Z         
2025-03-14T04:54:52.9382154Z 
2025-03-14T04:54:52.9382264Z class GraphModule(torch.nn.Module):
2025-03-14T04:54:52.9507288Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T04:54:52.9508205Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:54:52.9508644Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9509107Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9509557Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9510008Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9510450Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9510926Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9511423Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9511953Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9512429Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9512894Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9513309Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9513780Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9514252Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9514713Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9515161Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9515578Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9516065Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9516542Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9516975Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9517469Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9517902Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:54:52.9518414Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9518896Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9519395Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:54:52.9519868Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:54:52.9520298Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9520779Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9521251Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9521713Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9522121Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9522470Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9522882Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9523282Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9523729Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9524141Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9524507Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9524924Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9525327Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9525737Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9526117Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9526743Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9527151Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9527591Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9527982Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9528359Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9528721Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9529130Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9529542Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9529927Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9530330Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9530687Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9531102Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9531581Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9531977Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9532381Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9532776Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9533219Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9533700Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9534117Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9534531Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9534895Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9535339Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9535781Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9536186Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9536598Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9536966Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9537432Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9537870Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9538279Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9538681Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9539068Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:54:52.9539547Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9539985Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9540433Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:54:52.9540845Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:54:52.9541237Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9541681Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9542114Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9542528Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9542926Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9543285Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9543691Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9544120Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9544517Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9544893Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9545255Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9545669Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9546084Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9546487Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9546877Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9547244Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9547650Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9548074Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9548458Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9548841Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9549187Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9549600Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9550008Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9550390Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9550785Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9551134Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9551545Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9551950Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9552338Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9552724Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9553088Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9553515Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9553914Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9554316Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9554695Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9555049Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9555454Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9555854Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9556243Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9556615Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9556969Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9557387Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9557802Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9558192Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9558565Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9558920Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9559342Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9559766Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9560163Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9560648Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9561047Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9561443Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9561844Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9562220Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9562597Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9562951Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9563350Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9563797Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9564177Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9564552Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9564920Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:54:52.9565350Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9565779Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9566199Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:54:52.9566620Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:54:52.9566965Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9567398Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9567801Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9568199Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9568581Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9568930Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9569346Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9569750Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9570143Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9570536Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9570898Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9571341Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9571864Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9572331Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9572759Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9573134Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9573585Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9573997Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9574408Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9574859Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9575235Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9575675Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9576108Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9576519Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9576930Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9577319Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9577771Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9578211Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9578611Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9579021Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9579387Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9579852Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9580285Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9580709Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9581139Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9581511Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9581948Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9582373Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9582789Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9583196Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9583566Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9584003Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9584449Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9584847Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9585224Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9585585Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9586001Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9586417Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9586809Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9587197Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9587552Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9587989Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9588398Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9588791Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9589169Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9589526Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9589932Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9590337Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9590731Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9591119Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9591481Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9591885Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9592292Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9592675Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9593075Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9593433Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9593857Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9594264Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9594663Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9595050Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9595398Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9595810Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9596217Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9596593Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9596978Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9597342Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9597761Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9598163Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9598558Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9598934Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9599280Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9599712Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9600131Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9600522Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9600914Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9601269Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9601678Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9602085Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9602474Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9602848Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9603204Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9603612Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9604039Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9604434Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9604809Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9605163Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9605567Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9606000Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9606383Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9606807Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9607184Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9607631Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9608062Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9608467Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9608885Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9609252Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9609692Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9610127Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9610542Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9610949Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9611322Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9611832Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9612275Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9612688Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9613171Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9613545Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9614014Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9614449Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9614888Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9615295Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9615674Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9616135Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9616575Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9616998Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9617396Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9617790Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9618233Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9618675Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9619089Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9619492Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9619870Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9620325Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9620781Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9621201Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9621618Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9621996Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9622429Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9622866Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9623276Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9623696Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9624056Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9624465Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9624893Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9625286Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9625671Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9626026Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9626445Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9626876Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9627264Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9627666Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9628021Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9628462Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9628866Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9629263Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9629650Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9630001Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9630418Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9630824Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9631235Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9631616Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9631980Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9632399Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9632801Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9633211Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9633589Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9633980Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9634397Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9634819Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9635213Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9635586Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9635939Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9636346Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9636757Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9637140Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9637521Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9637899Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9638306Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9638712Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9639097Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9639481Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9639883Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9640297Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9640720Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9641105Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9641511Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9641864Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9642282Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9642687Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9643080Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9643464Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9643818Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9644255Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9644659Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9645053Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9645434Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9645790Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9646221Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9646623Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9647032Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9647407Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9647780Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9648190Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9648600Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9649001Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9649400Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9649776Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9650215Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9650699Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9651104Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9651589Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9651976Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9652409Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9652843Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9653283Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9653704Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9654098Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9654537Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9654996Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9655422Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9655842Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9656219Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9656665Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9657103Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9657518Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9657949Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9658325Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9658761Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9659199Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9659622Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9660057Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9660425Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9660985Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9661424Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9661889Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9662294Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9662676Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9663119Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9663545Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9663960Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9664358Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9664729Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9665192Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9665603Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9665995Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9666370Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9666729Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9667155Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9667572Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9667983Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9668386Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9668745Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9669152Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9669561Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9669947Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9670335Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9670684Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9671100Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9671525Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9671916Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9672301Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9672655Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9673075Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9673496Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9673892Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9674292Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9674642Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9675077Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9675479Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9675876Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9676256Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9676621Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9677041Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9677447Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9677851Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9678228Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9678584Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9678993Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9679398Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9679792Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9680184Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9680540Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9680964Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9681390Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9681784Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9682157Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9682512Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9682924Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9683335Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9683718Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9684100Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9684477Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9684889Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9685297Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9685681Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9686071Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9686441Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9686859Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9687283Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9687668Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9688078Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9688426Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9688844Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9689249Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9689641Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9690025Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9690377Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9690805Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9691233Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9691719Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9692127Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9692525Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9692989Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9693433Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9693870Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9694277Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9694672Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9695113Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9695555Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9695979Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9696389Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9696764Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9697203Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9697647Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9698084Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9698501Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9698880Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9699323Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9699765Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9700204Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9700624Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9701011Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9701458Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9701920Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9702322Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9702740Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9703115Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9703555Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9703956Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9704360Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9704786Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9705144Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9705560Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9705963Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9706377Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9706779Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9707180Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:54:52.9707607Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9708037Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9708462Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:54:52.9708849Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:54:52.9709201Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9709614Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9710013Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9710406Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9710778Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9711133Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9711552Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9711966Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9712359Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9712735Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9713093Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9713514Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9713923Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9714330Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9714709Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9715082Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:54:52.9715490Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9715899Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9716280Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:52.9716665Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:52.9717012Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:54:52.9717425Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9717846Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9718228Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:52.9718609Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:52.9718956Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:54:52.9719368Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:52.9719772Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:52.9720179Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:52.9720559Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:52.9720806Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T04:54:52.9721029Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T04:54:52.9721265Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T04:54:52.9721483Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T04:54:52.9721704Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T04:54:52.9721925Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T04:54:52.9722143Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T04:54:52.9722357Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T04:54:52.9722573Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T04:54:52.9722792Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T04:54:52.9723007Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T04:54:52.9723221Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T04:54:52.9723440Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T04:54:52.9723659Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T04:54:52.9723886Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T04:54:52.9724143Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T04:54:52.9724512Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:54:52.9724860Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T04:54:52.9725215Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T04:54:52.9725570Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T04:54:52.9725920Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T04:54:52.9726261Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:54:52.9726593Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:54:52.9726975Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:54:52.9727325Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:54:52.9727695Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:54:52.9728031Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:54:52.9728102Z         
2025-03-14T04:54:52.9728389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9728940Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9729004Z         
2025-03-14T04:54:52.9729293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9731082Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9731149Z         
2025-03-14T04:54:52.9731510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:54:52.9731660Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:54:52.9731734Z         
2025-03-14T04:54:52.9732136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:54:52.9732413Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:54:52.9732478Z         
2025-03-14T04:54:52.9732763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9733296Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9733370Z         
2025-03-14T04:54:52.9733657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9735489Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9735579Z         
2025-03-14T04:54:52.9735870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9736015Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:54:52.9736078Z         
2025-03-14T04:54:52.9736335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9736839Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9736903Z         
2025-03-14T04:54:52.9737172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9739018Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9739096Z         
2025-03-14T04:54:52.9739412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9739551Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:54:52.9739621Z         
2025-03-14T04:54:52.9739871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9740410Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9740488Z         
2025-03-14T04:54:52.9740761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9742595Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9742678Z         
2025-03-14T04:54:52.9742931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9743427Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:54:52.9743496Z         
2025-03-14T04:54:52.9743762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9745695Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9745768Z         
2025-03-14T04:54:52.9746049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9746199Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:54:52.9746262Z         
2025-03-14T04:54:52.9746550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9746712Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:54:52.9746782Z         
2025-03-14T04:54:52.9747036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9747552Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9747615Z         
2025-03-14T04:54:52.9747884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9749717Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9749782Z         
2025-03-14T04:54:52.9750070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9750210Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:54:52.9750280Z         
2025-03-14T04:54:52.9750525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9751026Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9751095Z         
2025-03-14T04:54:52.9751372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9753190Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9753262Z         
2025-03-14T04:54:52.9753559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9753703Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:54:52.9753785Z         
2025-03-14T04:54:52.9754043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9754539Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9754625Z         
2025-03-14T04:54:52.9754888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9756688Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9756759Z         
2025-03-14T04:54:52.9757031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9757187Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:54:52.9757248Z         
2025-03-14T04:54:52.9757537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9757681Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:54:52.9757752Z         
2025-03-14T04:54:52.9758016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9758516Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9758578Z         
2025-03-14T04:54:52.9758855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9760839Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9760931Z         
2025-03-14T04:54:52.9761227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9761374Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:54:52.9761457Z         
2025-03-14T04:54:52.9761709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9762211Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9762281Z         
2025-03-14T04:54:52.9762544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9764350Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9764422Z         
2025-03-14T04:54:52.9764736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9764881Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:54:52.9764942Z         
2025-03-14T04:54:52.9765199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9765695Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9765765Z         
2025-03-14T04:54:52.9766026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9767852Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9767941Z         
2025-03-14T04:54:52.9768219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9768396Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:54:52.9768457Z         
2025-03-14T04:54:52.9768746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9768895Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:54:52.9768965Z         
2025-03-14T04:54:52.9769214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9769719Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9769781Z         
2025-03-14T04:54:52.9770052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9771979Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9772050Z         
2025-03-14T04:54:52.9772381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9772548Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:54:52.9772627Z         
2025-03-14T04:54:52.9772914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9773487Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9773562Z         
2025-03-14T04:54:52.9773863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9775689Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9775790Z         
2025-03-14T04:54:52.9776081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9776231Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:54:52.9776292Z         
2025-03-14T04:54:52.9776549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9777050Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9777120Z         
2025-03-14T04:54:52.9777383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9779219Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9779291Z         
2025-03-14T04:54:52.9779545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9780058Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:54:52.9780120Z         
2025-03-14T04:54:52.9780391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9782288Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9782387Z         
2025-03-14T04:54:52.9782672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9782819Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:54:52.9782886Z         
2025-03-14T04:54:52.9783169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9783326Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:54:52.9783389Z         
2025-03-14T04:54:52.9783648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9784159Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9784232Z         
2025-03-14T04:54:52.9784494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9786320Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9786395Z         
2025-03-14T04:54:52.9786684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9786837Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:54:52.9786900Z         
2025-03-14T04:54:52.9787163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9787979Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9788069Z         
2025-03-14T04:54:52.9788348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9790207Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9790305Z         
2025-03-14T04:54:52.9790590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9790736Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:54:52.9790796Z         
2025-03-14T04:54:52.9791053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9791546Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9791609Z         
2025-03-14T04:54:52.9791870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9793654Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9793728Z         
2025-03-14T04:54:52.9794010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9794176Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:54:52.9794246Z         
2025-03-14T04:54:52.9794526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9794697Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:54:52.9794756Z         
2025-03-14T04:54:52.9795006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9795499Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9795585Z         
2025-03-14T04:54:52.9795852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9797640Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9797709Z         
2025-03-14T04:54:52.9797986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9798133Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:54:52.9798191Z         
2025-03-14T04:54:52.9798444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9798945Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9799015Z         
2025-03-14T04:54:52.9799276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9801053Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9801138Z         
2025-03-14T04:54:52.9801410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9801551Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:54:52.9801611Z         
2025-03-14T04:54:52.9801862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9802360Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9802428Z         
2025-03-14T04:54:52.9802691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9804512Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9804584Z         
2025-03-14T04:54:52.9804858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9805014Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:54:52.9805097Z         
2025-03-14T04:54:52.9805376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9805532Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:54:52.9805594Z         
2025-03-14T04:54:52.9805850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9806337Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9806407Z         
2025-03-14T04:54:52.9806669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9808518Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9808620Z         
2025-03-14T04:54:52.9808917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9809071Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:54:52.9809133Z         
2025-03-14T04:54:52.9809399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9809918Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9809990Z         
2025-03-14T04:54:52.9810279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9812393Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9812479Z         
2025-03-14T04:54:52.9812800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9812953Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:54:52.9813019Z         
2025-03-14T04:54:52.9813297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9813797Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9813872Z         
2025-03-14T04:54:52.9814139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9815982Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9816098Z         
2025-03-14T04:54:52.9816385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9816547Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:54:52.9816611Z         
2025-03-14T04:54:52.9816904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9817053Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:54:52.9817127Z         
2025-03-14T04:54:52.9817378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9817874Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9817944Z         
2025-03-14T04:54:52.9818210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9820027Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9820100Z         
2025-03-14T04:54:52.9820383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9820527Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:54:52.9820590Z         
2025-03-14T04:54:52.9820850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9821357Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9821441Z         
2025-03-14T04:54:52.9821700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9823491Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9823577Z         
2025-03-14T04:54:52.9823867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9824008Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:54:52.9824067Z         
2025-03-14T04:54:52.9824329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9824820Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9824890Z         
2025-03-14T04:54:52.9825157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9827013Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9827085Z         
2025-03-14T04:54:52.9827343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9827858Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:54:52.9827922Z         
2025-03-14T04:54:52.9828192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9830072Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9830151Z         
2025-03-14T04:54:52.9830438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9830573Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:54:52.9830645Z         
2025-03-14T04:54:52.9830925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9831076Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:54:52.9831139Z         
2025-03-14T04:54:52.9831394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9831874Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9831943Z         
2025-03-14T04:54:52.9832226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9834025Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9834100Z         
2025-03-14T04:54:52.9834386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9834542Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:54:52.9834613Z         
2025-03-14T04:54:52.9834866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9835378Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9835459Z         
2025-03-14T04:54:52.9835723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9837503Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9837574Z         
2025-03-14T04:54:52.9837863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9837990Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:54:52.9838060Z         
2025-03-14T04:54:52.9838306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9838812Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9838875Z         
2025-03-14T04:54:52.9839151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9840996Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9841086Z         
2025-03-14T04:54:52.9841371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9841535Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:54:52.9841605Z         
2025-03-14T04:54:52.9841893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9842040Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:54:52.9842120Z         
2025-03-14T04:54:52.9842386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9842868Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9842941Z         
2025-03-14T04:54:52.9843198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9845033Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9845107Z         
2025-03-14T04:54:52.9845392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9845550Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:54:52.9845614Z         
2025-03-14T04:54:52.9845873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9846363Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9846436Z         
2025-03-14T04:54:52.9846706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9848530Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9848619Z         
2025-03-14T04:54:52.9848929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9849119Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:54:52.9849183Z         
2025-03-14T04:54:52.9849463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9850000Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9850069Z         
2025-03-14T04:54:52.9850372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9852437Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9852524Z         
2025-03-14T04:54:52.9852873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9853031Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:54:52.9853105Z         
2025-03-14T04:54:52.9853416Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9853570Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:54:52.9853638Z         
2025-03-14T04:54:52.9853916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9854428Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9854502Z         
2025-03-14T04:54:52.9854785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9856697Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9856801Z         
2025-03-14T04:54:52.9857100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9857247Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:54:52.9857312Z         
2025-03-14T04:54:52.9857583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9858103Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9858177Z         
2025-03-14T04:54:52.9858455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9860392Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9860469Z         
2025-03-14T04:54:52.9860911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9861066Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:54:52.9861132Z         
2025-03-14T04:54:52.9861403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9861917Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9862028Z         
2025-03-14T04:54:52.9862305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9864138Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9864233Z         
2025-03-14T04:54:52.9864510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9864662Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:54:52.9864725Z         
2025-03-14T04:54:52.9865012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9865158Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:54:52.9865221Z         
2025-03-14T04:54:52.9865477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9865950Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9866020Z         
2025-03-14T04:54:52.9866285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9868122Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9868193Z         
2025-03-14T04:54:52.9868482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9868615Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:54:52.9868674Z         
2025-03-14T04:54:52.9868944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9869428Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9869511Z         
2025-03-14T04:54:52.9869777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9871604Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9871676Z         
2025-03-14T04:54:52.9871953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9872089Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:54:52.9872149Z         
2025-03-14T04:54:52.9872400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9872891Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9872959Z         
2025-03-14T04:54:52.9873235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9875072Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9875146Z         
2025-03-14T04:54:52.9875444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9875595Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:54:52.9875656Z         
2025-03-14T04:54:52.9875953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9876086Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:54:52.9876152Z         
2025-03-14T04:54:52.9876394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9876882Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9876949Z         
2025-03-14T04:54:52.9877204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9878981Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9879046Z         
2025-03-14T04:54:52.9879335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9879472Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:54:52.9879530Z         
2025-03-14T04:54:52.9879797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9880278Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9880343Z         
2025-03-14T04:54:52.9880599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9882398Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9882495Z         
2025-03-14T04:54:52.9882769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9882902Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:54:52.9882978Z         
2025-03-14T04:54:52.9883237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9883706Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9883774Z         
2025-03-14T04:54:52.9884037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9885827Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9885896Z         
2025-03-14T04:54:52.9886175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9886337Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:54:52.9886402Z         
2025-03-14T04:54:52.9886688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9886827Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:54:52.9886896Z         
2025-03-14T04:54:52.9887142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9887624Z         x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9887686Z         
2025-03-14T04:54:52.9887958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9889766Z         x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9889864Z         
2025-03-14T04:54:52.9890168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9890305Z         out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:54:52.9890380Z         
2025-03-14T04:54:52.9890646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9891175Z         x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9891240Z         
2025-03-14T04:54:52.9891607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9893509Z         x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9893575Z         
2025-03-14T04:54:52.9893868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9893996Z         out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:54:52.9894066Z         
2025-03-14T04:54:52.9894310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9894803Z         x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9894873Z         
2025-03-14T04:54:52.9895162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9897044Z         x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9897146Z         
2025-03-14T04:54:52.9897426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9897578Z         x_93 += out_51;  out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:54:52.9897638Z         
2025-03-14T04:54:52.9897925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9898063Z         out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:54:52.9898134Z         
2025-03-14T04:54:52.9898380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9898867Z         x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9898930Z         
2025-03-14T04:54:52.9899200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9901028Z         x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9901094Z         
2025-03-14T04:54:52.9901388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9901520Z         out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:54:52.9901588Z         
2025-03-14T04:54:52.9901835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9902346Z         x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9902424Z         
2025-03-14T04:54:52.9902693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9904476Z         x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9904555Z         
2025-03-14T04:54:52.9904849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9904978Z         out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:54:52.9905047Z         
2025-03-14T04:54:52.9905295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9905790Z         x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9905852Z         
2025-03-14T04:54:52.9906120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9907952Z         x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9908017Z         
2025-03-14T04:54:52.9908301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9908443Z         x_99 += out_55;  out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:54:52.9908527Z         
2025-03-14T04:54:52.9908810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9908967Z         out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:54:52.9909027Z         
2025-03-14T04:54:52.9909281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9909779Z         x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9909856Z         
2025-03-14T04:54:52.9910128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9911934Z         x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9912005Z         
2025-03-14T04:54:52.9912293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9912429Z         out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:54:52.9912497Z         
2025-03-14T04:54:52.9912744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9913263Z         x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9913328Z         
2025-03-14T04:54:52.9913598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9915393Z         x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9915465Z         
2025-03-14T04:54:52.9915779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9915912Z         out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:54:52.9915979Z         
2025-03-14T04:54:52.9916227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9916753Z         x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9916817Z         
2025-03-14T04:54:52.9917089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9918932Z         x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9919000Z         
2025-03-14T04:54:52.9919312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9919471Z         x_105 += out_59;  out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:54:52.9919546Z         
2025-03-14T04:54:52.9919855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9920001Z         out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:54:52.9920061Z         
2025-03-14T04:54:52.9920346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9920826Z         x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9920897Z         
2025-03-14T04:54:52.9921173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9922995Z         x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9923096Z         
2025-03-14T04:54:52.9923385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9923530Z         out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:54:52.9923593Z         
2025-03-14T04:54:52.9923853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9924347Z         x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9924409Z         
2025-03-14T04:54:52.9924682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9926510Z         x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9926585Z         
2025-03-14T04:54:52.9926875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9927009Z         out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:54:52.9927078Z         
2025-03-14T04:54:52.9927323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9927814Z         x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9927877Z         
2025-03-14T04:54:52.9928145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9930038Z         x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9930144Z         
2025-03-14T04:54:52.9930443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9930604Z         x_111 += out_63;  out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:54:52.9930675Z         
2025-03-14T04:54:52.9930986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9931137Z         out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:54:52.9931200Z         
2025-03-14T04:54:52.9931552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9932128Z         x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9932204Z         
2025-03-14T04:54:52.9932508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9934479Z         x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9934558Z         
2025-03-14T04:54:52.9934857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9935005Z         out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:54:52.9935074Z         
2025-03-14T04:54:52.9935349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9935890Z         x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9935981Z         
2025-03-14T04:54:52.9936266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9938182Z         x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9938275Z         
2025-03-14T04:54:52.9938577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9938730Z         out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:54:52.9938800Z         
2025-03-14T04:54:52.9939061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9939592Z         x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9939659Z         
2025-03-14T04:54:52.9939943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9941791Z         x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9941862Z         
2025-03-14T04:54:52.9942142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9942286Z         x_117 += out_67;  out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:54:52.9942354Z         
2025-03-14T04:54:52.9942649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9942793Z         out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:54:52.9942870Z         
2025-03-14T04:54:52.9943120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9943592Z         x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9943677Z         
2025-03-14T04:54:52.9943931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9945704Z         x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9945774Z         
2025-03-14T04:54:52.9946045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9946183Z         out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:54:52.9946243Z         
2025-03-14T04:54:52.9946488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9946975Z         x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9947045Z         
2025-03-14T04:54:52.9947307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9949109Z         x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9949181Z         
2025-03-14T04:54:52.9949458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9949608Z         out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:54:52.9949668Z         
2025-03-14T04:54:52.9949919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9950415Z         x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9950484Z         
2025-03-14T04:54:52.9950754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9952583Z         x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9952654Z         
2025-03-14T04:54:52.9952938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9953087Z         x_123 += out_71;  out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:54:52.9953155Z         
2025-03-14T04:54:52.9953446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9953595Z         out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:54:52.9953655Z         
2025-03-14T04:54:52.9953907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9954382Z         x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9954452Z         
2025-03-14T04:54:52.9954711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9956546Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9956680Z         
2025-03-14T04:54:52.9956962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9957101Z         out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:54:52.9957164Z         
2025-03-14T04:54:52.9957417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9957906Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9957975Z         
2025-03-14T04:54:52.9958236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9960056Z         x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9960128Z         
2025-03-14T04:54:52.9960413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9960638Z         out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:54:52.9960708Z         
2025-03-14T04:54:52.9960963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9961453Z         x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9961525Z         
2025-03-14T04:54:52.9961785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9963644Z         x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9963758Z         
2025-03-14T04:54:52.9964035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9964189Z         x_129 += out_75;  out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:54:52.9964251Z         
2025-03-14T04:54:52.9964537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9964671Z         out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:54:52.9964740Z         
2025-03-14T04:54:52.9964989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9965483Z         x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9965555Z         
2025-03-14T04:54:52.9965818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9967650Z         x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9967723Z         
2025-03-14T04:54:52.9968008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9968148Z         out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:54:52.9968212Z         
2025-03-14T04:54:52.9968471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9968971Z         x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9969055Z         
2025-03-14T04:54:52.9969314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9971186Z         x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9971275Z         
2025-03-14T04:54:52.9971637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9971793Z         out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:54:52.9971855Z         
2025-03-14T04:54:52.9972124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9972651Z         x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9972727Z         
2025-03-14T04:54:52.9973006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9974919Z         x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9974995Z         
2025-03-14T04:54:52.9975274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9975432Z         x_135 += out_79;  out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:54:52.9975495Z         
2025-03-14T04:54:52.9975799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9975938Z         out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:54:52.9976023Z         
2025-03-14T04:54:52.9976270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9976762Z         x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9976839Z         
2025-03-14T04:54:52.9977108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9978951Z         x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9979016Z         
2025-03-14T04:54:52.9979306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9979436Z         out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:54:52.9979507Z         
2025-03-14T04:54:52.9979751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9980260Z         x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9980330Z         
2025-03-14T04:54:52.9980597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9982443Z         x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9982516Z         
2025-03-14T04:54:52.9982801Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9982959Z         out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:54:52.9983020Z         
2025-03-14T04:54:52.9983276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9983767Z         x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9983856Z         
2025-03-14T04:54:52.9984118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9985951Z         x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9986022Z         
2025-03-14T04:54:52.9986302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9986457Z         x_141 += out_83;  out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:54:52.9986519Z         
2025-03-14T04:54:52.9986847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9986995Z         out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:54:52.9987066Z         
2025-03-14T04:54:52.9987333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9987818Z         x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9987883Z         
2025-03-14T04:54:52.9988165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9990026Z         x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9990124Z         
2025-03-14T04:54:52.9990415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9990546Z         out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:54:52.9990615Z         
2025-03-14T04:54:52.9990861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9991351Z         x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:52.9991420Z         
2025-03-14T04:54:52.9991683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9993524Z         x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9993589Z         
2025-03-14T04:54:52.9993881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9994021Z         out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:54:52.9994082Z         
2025-03-14T04:54:52.9994339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9994831Z         x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:52.9994900Z         
2025-03-14T04:54:52.9995170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:52.9997003Z         x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:52.9997103Z         
2025-03-14T04:54:52.9997382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:52.9997538Z         x_147 += out_87;  out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:54:52.9997601Z         
2025-03-14T04:54:52.9997887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:52.9998025Z         out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:54:52.9998091Z         
2025-03-14T04:54:52.9998340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:52.9998836Z         x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:52.9998898Z         
2025-03-14T04:54:52.9999166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0000994Z         x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0001058Z         
2025-03-14T04:54:53.0001357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0001495Z         out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:54:53.0001567Z         
2025-03-14T04:54:53.0001843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0002419Z         x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0002485Z         
2025-03-14T04:54:53.0002792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0004681Z         x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0004776Z         
2025-03-14T04:54:53.0005103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0005242Z         out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:54:53.0005316Z         
2025-03-14T04:54:53.0005589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0006161Z         x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0006241Z         
2025-03-14T04:54:53.0006534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0008567Z         x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0008646Z         
2025-03-14T04:54:53.0008956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0009132Z         x_153 += out_91;  out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:54:53.0009199Z         
2025-03-14T04:54:53.0009544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0009699Z         out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:54:53.0009791Z         
2025-03-14T04:54:53.0010077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0010637Z         x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0010724Z         
2025-03-14T04:54:53.0011038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0013155Z         x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0013234Z         
2025-03-14T04:54:53.0013551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0013687Z         out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:54:53.0013760Z         
2025-03-14T04:54:53.0014029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0014588Z         x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0014652Z         
2025-03-14T04:54:53.0014925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0016851Z         x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0016919Z         
2025-03-14T04:54:53.0017223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0017384Z         out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:54:53.0017455Z         
2025-03-14T04:54:53.0017713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0018254Z         x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0018335Z         
2025-03-14T04:54:53.0018623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0020535Z         x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0020601Z         
2025-03-14T04:54:53.0020906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0021061Z         x_159 += out_95;  out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:54:53.0021136Z         
2025-03-14T04:54:53.0021448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0021603Z         out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:54:53.0021666Z         
2025-03-14T04:54:53.0021937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0022450Z         x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0022518Z         
2025-03-14T04:54:53.0022799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0024736Z         x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0024835Z         
2025-03-14T04:54:53.0025173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0025338Z         out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:54:53.0025409Z         
2025-03-14T04:54:53.0025669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0026197Z         x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0026264Z         
2025-03-14T04:54:53.0026552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0028475Z         x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0028553Z         
2025-03-14T04:54:53.0028862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0029007Z         out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:54:53.0029080Z         
2025-03-14T04:54:53.0029346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0029876Z         x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0029940Z         
2025-03-14T04:54:53.0030229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0032181Z         x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0032278Z         
2025-03-14T04:54:53.0032580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0032744Z         x_165 += out_99;  out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:54:53.0032818Z         
2025-03-14T04:54:53.0033117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0033276Z         out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:54:53.0033340Z         
2025-03-14T04:54:53.0033611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0034153Z         x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0034229Z         
2025-03-14T04:54:53.0034526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0036502Z         x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0036584Z         
2025-03-14T04:54:53.0036897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0037058Z         out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:54:53.0037122Z         
2025-03-14T04:54:53.0037389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0037928Z         x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0037992Z         
2025-03-14T04:54:53.0038295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0040293Z         x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0040389Z         
2025-03-14T04:54:53.0040723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0040867Z         out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:54:53.0040940Z         
2025-03-14T04:54:53.0041204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0041773Z         x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0041843Z         
2025-03-14T04:54:53.0042140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0044209Z         x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0044290Z         
2025-03-14T04:54:53.0044610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0044789Z         x_171 += out_103;  out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:54:53.0044866Z         
2025-03-14T04:54:53.0045199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0045369Z         out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:54:53.0045454Z         
2025-03-14T04:54:53.0045744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0046284Z         x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0046377Z         
2025-03-14T04:54:53.0046673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0048739Z         x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0048822Z         
2025-03-14T04:54:53.0049148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0049310Z         out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:54:53.0049380Z         
2025-03-14T04:54:53.0049673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0050254Z         x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0050333Z         
2025-03-14T04:54:53.0050642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0052807Z         x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0052893Z         
2025-03-14T04:54:53.0053214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0053387Z         out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:54:53.0053459Z         
2025-03-14T04:54:53.0053730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0054288Z         x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0054374Z         
2025-03-14T04:54:53.0054676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0056655Z         x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0056731Z         
2025-03-14T04:54:53.0057031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0057200Z         x_177 += out_107;  out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:54:53.0057272Z         
2025-03-14T04:54:53.0057600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0057770Z         out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:54:53.0057837Z         
2025-03-14T04:54:53.0058127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0058689Z         x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0058766Z         
2025-03-14T04:54:53.0059042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0061106Z         x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0061210Z         
2025-03-14T04:54:53.0061535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0061685Z         out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:54:53.0061751Z         
2025-03-14T04:54:53.0062020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0062535Z         x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0062610Z         
2025-03-14T04:54:53.0062886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0064833Z         x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0064910Z         
2025-03-14T04:54:53.0065210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0065358Z         out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:54:53.0065422Z         
2025-03-14T04:54:53.0065694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0066218Z         x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0066290Z         
2025-03-14T04:54:53.0066576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0068584Z         x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0068687Z         
2025-03-14T04:54:53.0068991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0069154Z         x_183 += out_111;  out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:54:53.0069228Z         
2025-03-14T04:54:53.0069526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0069683Z         out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:54:53.0069746Z         
2025-03-14T04:54:53.0070013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0070528Z         x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0070602Z         
2025-03-14T04:54:53.0070879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0072824Z         x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0072901Z         
2025-03-14T04:54:53.0073204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0073352Z         out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:54:53.0073418Z         
2025-03-14T04:54:53.0073682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0074216Z         x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0074304Z         
2025-03-14T04:54:53.0074579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0076495Z         x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0076587Z         
2025-03-14T04:54:53.0076887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0077030Z         out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:54:53.0077096Z         
2025-03-14T04:54:53.0077360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0077884Z         x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0077962Z         
2025-03-14T04:54:53.0078242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0080192Z         x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0080271Z         
2025-03-14T04:54:53.0080568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0080743Z         x_189 += out_115;  out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:54:53.0080809Z         
2025-03-14T04:54:53.0081138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0081280Z         out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:54:53.0081364Z         
2025-03-14T04:54:53.0081609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0082126Z         x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0082215Z         
2025-03-14T04:54:53.0082489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0084410Z         x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0084484Z         
2025-03-14T04:54:53.0084793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0084947Z         out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191);  x_191 = None
2025-03-14T04:54:53.0085017Z         
2025-03-14T04:54:53.0085296Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0085857Z         x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0085936Z         
2025-03-14T04:54:53.0086234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0088308Z         x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0088390Z         
2025-03-14T04:54:53.0088721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0088899Z         out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193);  x_193 = None
2025-03-14T04:54:53.0088969Z         
2025-03-14T04:54:53.0089261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0089829Z         x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0089924Z         
2025-03-14T04:54:53.0090226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0092365Z         x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0092456Z         
2025-03-14T04:54:53.0092751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0093312Z         x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:54:53.0093381Z         
2025-03-14T04:54:53.0093663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0095650Z         x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0095717Z         
2025-03-14T04:54:53.0096045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0096205Z         x_195 += x_197;  out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195;  x_195 = x_197 = None
2025-03-14T04:54:53.0096293Z         
2025-03-14T04:54:53.0096589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0096744Z         out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122);  out_122 = None
2025-03-14T04:54:53.0096810Z         
2025-03-14T04:54:53.0097103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0097613Z         x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0097679Z         
2025-03-14T04:54:53.0097968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0099873Z         x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0099952Z         
2025-03-14T04:54:53.0100258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0100417Z         out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199);  x_199 = None
2025-03-14T04:54:53.0100492Z         
2025-03-14T04:54:53.0100756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0101285Z         x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0101351Z         
2025-03-14T04:54:53.0101639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0103656Z         x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0103752Z         
2025-03-14T04:54:53.0104075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0104242Z         out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201);  x_201 = None
2025-03-14T04:54:53.0104319Z         
2025-03-14T04:54:53.0104595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0105152Z         x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0105221Z         
2025-03-14T04:54:53.0105519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0107553Z         x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0107626Z         
2025-03-14T04:54:53.0107966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0108151Z         x_203 += out_123;  out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203;  x_203 = out_123 = None
2025-03-14T04:54:53.0108239Z         
2025-03-14T04:54:53.0108551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0108717Z         out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126);  out_126 = None
2025-03-14T04:54:53.0108785Z         
2025-03-14T04:54:53.0109069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0109603Z         x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0109681Z         
2025-03-14T04:54:53.0109987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0111986Z         x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0112097Z         
2025-03-14T04:54:53.0112410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0112567Z         out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205);  x_205 = None
2025-03-14T04:54:53.0112632Z         
2025-03-14T04:54:53.0112916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0113468Z         x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0113539Z         
2025-03-14T04:54:53.0113837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0115832Z         x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0115912Z         
2025-03-14T04:54:53.0116235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0116388Z         out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207);  x_207 = None
2025-03-14T04:54:53.0116466Z         
2025-03-14T04:54:53.0116743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0117305Z         x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0117391Z         
2025-03-14T04:54:53.0117691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0119701Z         x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0119797Z         
2025-03-14T04:54:53.0120112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0120283Z         x_209 += out_127;  out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209;  x_209 = out_127 = None
2025-03-14T04:54:53.0120359Z         
2025-03-14T04:54:53.0120671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0120837Z         out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130);  out_130 = None
2025-03-14T04:54:53.0120905Z         
2025-03-14T04:54:53.0121189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0121848Z         x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T04:54:53.0121922Z         
2025-03-14T04:54:53.0122182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0122800Z         x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T04:54:53.0122876Z         
2025-03-14T04:54:53.0123329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:54:53.0123622Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest');  x_210 = None
2025-03-14T04:54:53.0123687Z         
2025-03-14T04:54:53.0123958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0124587Z         x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T04:54:53.0124659Z         
2025-03-14T04:54:53.0125033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:54:53.0125266Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features;  x_212 = top_down_features = None
2025-03-14T04:54:53.0125331Z         
2025-03-14T04:54:53.0125597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0126235Z         x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T04:54:53.0126313Z         
2025-03-14T04:54:53.0126767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:54:53.0127136Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T04:54:53.0127214Z         
2025-03-14T04:54:53.0127495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0128157Z         x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T04:54:53.0128225Z         
2025-03-14T04:54:53.0128622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:54:53.0128854Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1;  x_214 = top_down_features_1 = None
2025-03-14T04:54:53.0128931Z         
2025-03-14T04:54:53.0129224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0129904Z         x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T04:54:53.0129973Z         
2025-03-14T04:54:53.0130427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:54:53.0130790Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T04:54:53.0130866Z         
2025-03-14T04:54:53.0131155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0131901Z         x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T04:54:53.0132019Z         
2025-03-14T04:54:53.0132422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:54:53.0132666Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2;  x_216 = top_down_features_2 = None
2025-03-14T04:54:53.0132735Z         
2025-03-14T04:54:53.0133046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0133710Z         x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T04:54:53.0133782Z         
2025-03-14T04:54:53.0134147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T04:54:53.0134367Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T04:54:53.0134426Z         
2025-03-14T04:54:53.0134878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.0135037Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T04:54:53.0135099Z         
2025-03-14T04:54:53.0135403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0135542Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:54:53.0135611Z         
2025-03-14T04:54:53.0136047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.0136221Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T04:54:53.0136285Z         
2025-03-14T04:54:53.0136581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0136721Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:54:53.0136788Z         
2025-03-14T04:54:53.0137159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:54:53.0137346Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:54:53.0137448Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T04:54:53.0137584Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:54:53.0137654Z         
2025-03-14T04:54:53.0138008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:54:53.0138141Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:54:53.0138230Z         
2025-03-14T04:54:53.0138573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:54:53.0138726Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:54:53.0138792Z         
2025-03-14T04:54:53.0139198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:54:53.0139424Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:54:53.0139517Z         
2025-03-14T04:54:53.0139962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:54:53.0140102Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:54:53.0140548Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:54:53.0140690Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:54:53.0140811Z         x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T04:54:53.0140885Z         
2025-03-14T04:54:53.0141344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.0141509Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T04:54:53.0141572Z         
2025-03-14T04:54:53.0141890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0142048Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:54:53.0142111Z         
2025-03-14T04:54:53.0142576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.0142750Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T04:54:53.0142828Z         
2025-03-14T04:54:53.0143135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0143287Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T04:54:53.0143352Z         
2025-03-14T04:54:53.0143757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:54:53.0143965Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T04:54:53.0144078Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T04:54:53.0144208Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T04:54:53.0144282Z         
2025-03-14T04:54:53.0144633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:54:53.0144817Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T04:54:53.0144884Z         
2025-03-14T04:54:53.0145232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:54:53.0145379Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T04:54:53.0145451Z         
2025-03-14T04:54:53.0145854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:54:53.0146114Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T04:54:53.0146179Z         
2025-03-14T04:54:53.0146622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:54:53.0146759Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T04:54:53.0147212Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T04:54:53.0147342Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T04:54:53.0147473Z         x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T04:54:53.0147541Z         
2025-03-14T04:54:53.0148003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.0148155Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T04:54:53.0148228Z         
2025-03-14T04:54:53.0148540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0148695Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T04:54:53.0148766Z         
2025-03-14T04:54:53.0149235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.0149394Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T04:54:53.0149459Z         
2025-03-14T04:54:53.0149775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0149916Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T04:54:53.0149992Z         
2025-03-14T04:54:53.0150404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:54:53.0150633Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T04:54:53.0150735Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T04:54:53.0150870Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T04:54:53.0150934Z         
2025-03-14T04:54:53.0151310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:54:53.0151440Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T04:54:53.0151513Z         
2025-03-14T04:54:53.0151855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:54:53.0152005Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T04:54:53.0152070Z         
2025-03-14T04:54:53.0152543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:54:53.0152861Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T04:54:53.0152937Z         
2025-03-14T04:54:53.0153389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:54:53.0153533Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T04:54:53.0153974Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T04:54:53.0154182Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T04:54:53.0154316Z         x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T04:54:53.0154388Z         
2025-03-14T04:54:53.0154934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.0155092Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T04:54:53.0155156Z         
2025-03-14T04:54:53.0155464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0155609Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T04:54:53.0155671Z         
2025-03-14T04:54:53.0156139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.0156285Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T04:54:53.0156357Z         
2025-03-14T04:54:53.0156653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0156795Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T04:54:53.0156859Z         
2025-03-14T04:54:53.0157238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:54:53.0157432Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T04:54:53.0157541Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T04:54:53.0157660Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T04:54:53.0157731Z         
2025-03-14T04:54:53.0158076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:54:53.0158206Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T04:54:53.0158269Z         
2025-03-14T04:54:53.0158623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:54:53.0158745Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T04:54:53.0158814Z         
2025-03-14T04:54:53.0159202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:54:53.0159438Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T04:54:53.0159500Z         
2025-03-14T04:54:53.0159925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:54:53.0160056Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T04:54:53.0160499Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T04:54:53.0160730Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T04:54:53.0160864Z         x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T04:54:53.0160930Z         
2025-03-14T04:54:53.0161397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.0161543Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:54:53.0161616Z         
2025-03-14T04:54:53.0161926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0162074Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T04:54:53.0162146Z         
2025-03-14T04:54:53.0162645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.0162805Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:54:53.0162870Z         
2025-03-14T04:54:53.0163194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0163337Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T04:54:53.0163408Z         
2025-03-14T04:54:53.0163800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:54:53.0164011Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T04:54:53.0164112Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T04:54:53.0164240Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T04:54:53.0164307Z         
2025-03-14T04:54:53.0164695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:54:53.0164828Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T04:54:53.0164923Z         
2025-03-14T04:54:53.0165261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:54:53.0165397Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T04:54:53.0165462Z         
2025-03-14T04:54:53.0165874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:54:53.0166122Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T04:54:53.0166196Z         
2025-03-14T04:54:53.0166635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:54:53.0166778Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T04:54:53.0167245Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T04:54:53.0167393Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T04:54:53.0167521Z         x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T04:54:53.0167598Z         
2025-03-14T04:54:53.0167938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:54:53.0168088Z         tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32);  x_218 = None
2025-03-14T04:54:53.0168230Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32);  x_219 = None
2025-03-14T04:54:53.0168372Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32);  x_220 = None
2025-03-14T04:54:53.0168505Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32);  x_221 = None
2025-03-14T04:54:53.0168642Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32);  x_222 = None
2025-03-14T04:54:53.0168710Z         
2025-03-14T04:54:53.0169021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0169601Z         x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_217 = None
2025-03-14T04:54:53.0169672Z         
2025-03-14T04:54:53.0169998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:54:53.0170218Z         x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False);  x_223 = None
2025-03-14T04:54:53.0170294Z         
2025-03-14T04:54:53.0170732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:54:53.0171340Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:54:53.0171412Z         
2025-03-14T04:54:53.0171904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:54:53.0172531Z         x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_224 = None
2025-03-14T04:54:53.0172633Z         
2025-03-14T04:54:53.0172926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0173450Z         x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_215 = None
2025-03-14T04:54:53.0173524Z         
2025-03-14T04:54:53.0173810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:54:53.0174022Z         x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False);  x_225 = None
2025-03-14T04:54:53.0174087Z         
2025-03-14T04:54:53.0174491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:54:53.0175043Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:54:53.0175119Z         
2025-03-14T04:54:53.0175493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:54:53.0176112Z         x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_226 = None
2025-03-14T04:54:53.0176181Z         
2025-03-14T04:54:53.0176455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0176960Z         x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_213 = None
2025-03-14T04:54:53.0177034Z         
2025-03-14T04:54:53.0177317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:54:53.0177503Z         x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False);  x_227 = None
2025-03-14T04:54:53.0177563Z         
2025-03-14T04:54:53.0177940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:54:53.0178466Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:54:53.0178529Z         
2025-03-14T04:54:53.0178906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:54:53.0179406Z         x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_228 = None
2025-03-14T04:54:53.0179493Z         
2025-03-14T04:54:53.0179741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0180214Z         x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_211 = None
2025-03-14T04:54:53.0180277Z         
2025-03-14T04:54:53.0180551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:54:53.0180725Z         x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False);  x_229 = None
2025-03-14T04:54:53.0180795Z         
2025-03-14T04:54:53.0181162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:54:53.0181665Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:54:53.0181737Z         
2025-03-14T04:54:53.0182098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:54:53.0182617Z         x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_230 = None
2025-03-14T04:54:53.0182682Z         
2025-03-14T04:54:53.0182942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0183726Z         x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:54:53.0183797Z         
2025-03-14T04:54:53.0184064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:54:53.0184246Z         x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False);  x_231 = None
2025-03-14T04:54:53.0184309Z         
2025-03-14T04:54:53.0184711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:54:53.0185715Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:54:53.0185809Z         
2025-03-14T04:54:53.0186179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:54:53.0187018Z         x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:54:53.0187093Z         
2025-03-14T04:54:53.0187428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:54:53.0187605Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:54:53.0187750Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:54:53.0187921Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T04:54:53.0188065Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T04:54:53.0188223Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T04:54:53.0188366Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T04:54:53.0188515Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T04:54:53.0188655Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T04:54:53.0188799Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T04:54:53.0188951Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T04:54:53.0189013Z         
2025-03-14T04:54:53.0189449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:54:53.0189626Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304);  x_233 = None
2025-03-14T04:54:53.0189821Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T04:54:53.0189999Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T04:54:53.0190168Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152);  x_234 = None
2025-03-14T04:54:53.0190344Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T04:54:53.0190524Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T04:54:53.0190683Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76);  x_235 = None
2025-03-14T04:54:53.0190856Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T04:54:53.0191079Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T04:54:53.0191226Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38);  x_236 = None
2025-03-14T04:54:53.0191387Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T04:54:53.0191576Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T04:54:53.0191716Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19);  x_237 = None
2025-03-14T04:54:53.0191881Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T04:54:53.0192044Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T04:54:53.0192115Z         
2025-03-14T04:54:53.0192528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:54:53.0192737Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:54:53.0192811Z         
2025-03-14T04:54:53.0193268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:54:53.0193429Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:54:53.0193576Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:54:53.0193719Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:54:53.0193781Z         
2025-03-14T04:54:53.0194163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:54:53.0194329Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:54:53.0194412Z         
2025-03-14T04:54:53.0194730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:54:53.0194879Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:54:53.0194939Z         
2025-03-14T04:54:53.0195259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:54:53.0195391Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:54:53.0195522Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:54:53.0195674Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:54:53.0195743Z         
2025-03-14T04:54:53.0196064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:54:53.0196196Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:54:53.0196334Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:54:53.0196496Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T04:54:53.0196558Z         
2025-03-14T04:54:53.0196872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:54:53.0197011Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:54:53.0197105Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T04:54:53.0197231Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T04:54:53.0197325Z         
2025-03-14T04:54:53.0197636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:54:53.0197796Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:54:53.0197886Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T04:54:53.0198027Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T04:54:53.0198094Z         
2025-03-14T04:54:53.0198442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:54:53.0198602Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:54:53.0198726Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T04:54:53.0198794Z         
2025-03-14T04:54:53.0199106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:54:53.0199260Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:54:53.0199384Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T04:54:53.0199449Z         
2025-03-14T04:54:53.0199751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:54:53.0199914Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:54:53.0200026Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T04:54:53.0200098Z         
2025-03-14T04:54:53.0200418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:54:53.0200613Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:54:53.0200724Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T04:54:53.0200793Z         
2025-03-14T04:54:53.0201128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:54:53.0201277Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:54:53.0201340Z         
2025-03-14T04:54:53.0201688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:54:53.0201830Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:54:53.0201903Z         
2025-03-14T04:54:53.0202260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:54:53.0202427Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:54:53.0202559Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T04:54:53.0202741Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:54:53.0202882Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T04:54:53.0202952Z         
2025-03-14T04:54:53.0203302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:54:53.0203465Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:54:53.0203590Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T04:54:53.0203753Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:54:53.0203890Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T04:54:53.0203961Z         
2025-03-14T04:54:53.0204295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:54:53.0204438Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:54:53.0204600Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:54:53.0204743Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T04:54:53.0204805Z         
2025-03-14T04:54:53.0205152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:54:53.0205272Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:54:53.0205447Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:54:53.0205584Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T04:54:53.0205654Z         
2025-03-14T04:54:53.0205972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:54:53.0206075Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:54:53.0206220Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:54:53.0206285Z         
2025-03-14T04:54:53.0206615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:54:53.0206708Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:54:53.0206832Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:54:53.0206895Z         
2025-03-14T04:54:53.0207211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:54:53.0207326Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:54:53.0207464Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:54:53.0207527Z         
2025-03-14T04:54:53.0207843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:54:53.0207957Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:54:53.0208110Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:54:53.0208174Z         
2025-03-14T04:54:53.0208537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:54:53.0208737Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:54:53.0208808Z         
2025-03-14T04:54:53.0209152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:54:53.0209347Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T04:54:53.0209411Z         
2025-03-14T04:54:53.0209819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:54:53.0210005Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:54:53.0210080Z         
2025-03-14T04:54:53.0210503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:54:53.0210743Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T04:54:53.0210808Z         
2025-03-14T04:54:53.0211260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:54:53.0211510Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T04:54:53.0211687Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T04:54:53.0211838Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T04:54:53.0211919Z         
2025-03-14T04:54:53.0212339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:54:53.0212535Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T04:54:53.0212627Z         
2025-03-14T04:54:53.0212974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:54:53.0213130Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T04:54:53.0213203Z         
2025-03-14T04:54:53.0213532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:54:53.0213676Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T04:54:53.0213805Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:54:53.0213970Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T04:54:53.0214035Z         
2025-03-14T04:54:53.0214367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:54:53.0214502Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T04:54:53.0214641Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T04:54:53.0214806Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T04:54:53.0214869Z         
2025-03-14T04:54:53.0215192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:54:53.0215332Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:54:53.0215433Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T04:54:53.0215568Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T04:54:53.0215654Z         
2025-03-14T04:54:53.0215972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:54:53.0216132Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T04:54:53.0216227Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T04:54:53.0216365Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T04:54:53.0216428Z         
2025-03-14T04:54:53.0216745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:54:53.0216900Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:54:53.0217023Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T04:54:53.0217089Z         
2025-03-14T04:54:53.0217396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:54:53.0217549Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:54:53.0217671Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T04:54:53.0217731Z         
2025-03-14T04:54:53.0218037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:54:53.0220858Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:54:53.0220995Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T04:54:53.0221059Z         
2025-03-14T04:54:53.0221394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:54:53.0221581Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T04:54:53.0221704Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T04:54:53.0221764Z         
2025-03-14T04:54:53.0222107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:54:53.0222248Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T04:54:53.0222356Z         
2025-03-14T04:54:53.0222691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:54:53.0222826Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T04:54:53.0222893Z         
2025-03-14T04:54:53.0223267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:54:53.0223415Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T04:54:53.0223539Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T04:54:53.0223725Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T04:54:53.0223865Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T04:54:53.0223935Z         
2025-03-14T04:54:53.0224283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:54:53.0224430Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T04:54:53.0224557Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T04:54:53.0224709Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T04:54:53.0224853Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T04:54:53.0224916Z         
2025-03-14T04:54:53.0225250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:54:53.0225366Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T04:54:53.0225536Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T04:54:53.0225669Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T04:54:53.0225739Z         
2025-03-14T04:54:53.0226071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:54:53.0226191Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T04:54:53.0226358Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T04:54:53.0226498Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T04:54:53.0226637Z         
2025-03-14T04:54:53.0226957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:54:53.0227069Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T04:54:53.0227197Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T04:54:53.0227261Z         
2025-03-14T04:54:53.0227576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:54:53.0227669Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T04:54:53.0227792Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T04:54:53.0227857Z         
2025-03-14T04:54:53.0228168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:54:53.0228289Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T04:54:53.0228430Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T04:54:53.0228491Z         
2025-03-14T04:54:53.0228795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:54:53.0228926Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T04:54:53.0229063Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T04:54:53.0229124Z         
2025-03-14T04:54:53.0229491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:54:53.0229684Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T04:54:53.0229754Z         
2025-03-14T04:54:53.0230083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:54:53.0230256Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T04:54:53.0230319Z         
2025-03-14T04:54:53.0230703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:54:53.0230880Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T04:54:53.0230951Z         
2025-03-14T04:54:53.0231347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:54:53.0231557Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T04:54:53.0231619Z         
2025-03-14T04:54:53.0232058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:54:53.0232216Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T04:54:53.0232364Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T04:54:53.0232510Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T04:54:53.0232607Z         
2025-03-14T04:54:53.0232990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:54:53.0233168Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T04:54:53.0233236Z         
2025-03-14T04:54:53.0233546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:54:53.0233693Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T04:54:53.0233753Z         
2025-03-14T04:54:53.0234074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:54:53.0234203Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T04:54:53.0234329Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:54:53.0234477Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T04:54:53.0234548Z         
2025-03-14T04:54:53.0234862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:54:53.0235006Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T04:54:53.0235125Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T04:54:53.0235279Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T04:54:53.0235360Z         
2025-03-14T04:54:53.0235679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:54:53.0235798Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:54:53.0235893Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T04:54:53.0236023Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T04:54:53.0236094Z         
2025-03-14T04:54:53.0236407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:54:53.0236560Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T04:54:53.0236650Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T04:54:53.0236787Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T04:54:53.0236847Z         
2025-03-14T04:54:53.0237159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:54:53.0237311Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:54:53.0237431Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T04:54:53.0237491Z         
2025-03-14T04:54:53.0237800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:54:53.0237950Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:54:53.0238066Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T04:54:53.0238128Z         
2025-03-14T04:54:53.0238434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:54:53.0238603Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:54:53.0238719Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T04:54:53.0238794Z         
2025-03-14T04:54:53.0239103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:54:53.0239295Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T04:54:53.0239402Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T04:54:53.0239470Z         
2025-03-14T04:54:53.0239803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:54:53.0239950Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T04:54:53.0240010Z         
2025-03-14T04:54:53.0240346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:54:53.0240479Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T04:54:53.0240546Z         
2025-03-14T04:54:53.0240900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:54:53.0241043Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T04:54:53.0241180Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T04:54:53.0241340Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T04:54:53.0241481Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T04:54:53.0241552Z         
2025-03-14T04:54:53.0241901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:54:53.0242046Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T04:54:53.0242170Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T04:54:53.0242328Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T04:54:53.0242469Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T04:54:53.0242542Z         
2025-03-14T04:54:53.0242878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:54:53.0243003Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T04:54:53.0243168Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T04:54:53.0243311Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T04:54:53.0243377Z         
2025-03-14T04:54:53.0243725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:54:53.0243841Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T04:54:53.0244028Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T04:54:53.0244178Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T04:54:53.0244246Z         
2025-03-14T04:54:53.0244577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:54:53.0244680Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T04:54:53.0244794Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T04:54:53.0244862Z         
2025-03-14T04:54:53.0245168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:54:53.0245265Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T04:54:53.0245376Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T04:54:53.0245443Z         
2025-03-14T04:54:53.0245744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:54:53.0245865Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T04:54:53.0245996Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T04:54:53.0246064Z         
2025-03-14T04:54:53.0246381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:54:53.0246501Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T04:54:53.0246633Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T04:54:53.0246710Z         
2025-03-14T04:54:53.0247058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:54:53.0247243Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T04:54:53.0247313Z         
2025-03-14T04:54:53.0247637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:54:53.0247804Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T04:54:53.0247866Z         
2025-03-14T04:54:53.0248241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:54:53.0248411Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T04:54:53.0248478Z         
2025-03-14T04:54:53.0248871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:54:53.0249087Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T04:54:53.0249149Z         
2025-03-14T04:54:53.0249591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:54:53.0249738Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T04:54:53.0249897Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T04:54:53.0250057Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T04:54:53.0250126Z         
2025-03-14T04:54:53.0250520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:54:53.0250698Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T04:54:53.0250760Z         
2025-03-14T04:54:53.0251082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:54:53.0251228Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T04:54:53.0251308Z         
2025-03-14T04:54:53.0251763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:54:53.0251927Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T04:54:53.0252065Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:54:53.0252240Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T04:54:53.0252310Z         
2025-03-14T04:54:53.0252678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:54:53.0252830Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T04:54:53.0252962Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T04:54:53.0253143Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T04:54:53.0253216Z         
2025-03-14T04:54:53.0253531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:54:53.0253660Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:54:53.0253760Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T04:54:53.0253893Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T04:54:53.0253964Z         
2025-03-14T04:54:53.0254281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:54:53.0254435Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T04:54:53.0254527Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T04:54:53.0254664Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T04:54:53.0254728Z         
2025-03-14T04:54:53.0255040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:54:53.0255196Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:54:53.0255399Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T04:54:53.0255466Z         
2025-03-14T04:54:53.0255785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:54:53.0255938Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:54:53.0256056Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T04:54:53.0256120Z         
2025-03-14T04:54:53.0256432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:54:53.0256601Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:54:53.0256803Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T04:54:53.0256877Z         
2025-03-14T04:54:53.0257294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:54:53.0257526Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T04:54:53.0257646Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T04:54:53.0257711Z         
2025-03-14T04:54:53.0258061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:54:53.0258201Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T04:54:53.0258272Z         
2025-03-14T04:54:53.0258609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:54:53.0258751Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T04:54:53.0258813Z         
2025-03-14T04:54:53.0259186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:54:53.0259340Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T04:54:53.0259471Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T04:54:53.0259631Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T04:54:53.0259779Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T04:54:53.0259852Z         
2025-03-14T04:54:53.0260205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:54:53.0260348Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T04:54:53.0260470Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T04:54:53.0260821Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T04:54:53.0260965Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T04:54:53.0261039Z         
2025-03-14T04:54:53.0261383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:54:53.0261507Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T04:54:53.0261669Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T04:54:53.0261811Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T04:54:53.0261876Z         
2025-03-14T04:54:53.0262224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:54:53.0262337Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T04:54:53.0262511Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T04:54:53.0262695Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T04:54:53.0262768Z         
2025-03-14T04:54:53.0263105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:54:53.0263209Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T04:54:53.0263326Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T04:54:53.0263397Z         
2025-03-14T04:54:53.0263723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:54:53.0263825Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T04:54:53.0263936Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T04:54:53.0264006Z         
2025-03-14T04:54:53.0264305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:54:53.0264428Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T04:54:53.0264558Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T04:54:53.0264629Z         
2025-03-14T04:54:53.0264952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:54:53.0265075Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T04:54:53.0265226Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T04:54:53.0265298Z         
2025-03-14T04:54:53.0265640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:54:53.0265833Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T04:54:53.0265912Z         
2025-03-14T04:54:53.0266238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:54:53.0266406Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T04:54:53.0266465Z         
2025-03-14T04:54:53.0266846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:54:53.0267011Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T04:54:53.0267081Z         
2025-03-14T04:54:53.0267474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:54:53.0267682Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T04:54:53.0267744Z         
2025-03-14T04:54:53.0268177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:54:53.0268318Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T04:54:53.0268471Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T04:54:53.0268634Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T04:54:53.0268695Z         
2025-03-14T04:54:53.0269081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:54:53.0269246Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T04:54:53.0269316Z         
2025-03-14T04:54:53.0269622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:54:53.0269766Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T04:54:53.0269827Z         
2025-03-14T04:54:53.0270143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:54:53.0270270Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T04:54:53.0270399Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:54:53.0270540Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T04:54:53.0270608Z         
2025-03-14T04:54:53.0270935Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:54:53.0271063Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T04:54:53.0271177Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T04:54:53.0271371Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T04:54:53.0271432Z         
2025-03-14T04:54:53.0271749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:54:53.0271870Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:54:53.0271963Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T04:54:53.0272091Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T04:54:53.0272160Z         
2025-03-14T04:54:53.0272465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:54:53.0272612Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T04:54:53.0272701Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T04:54:53.0272832Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T04:54:53.0272893Z         
2025-03-14T04:54:53.0273203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:54:53.0273353Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:54:53.0273472Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T04:54:53.0273534Z         
2025-03-14T04:54:53.0273845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:54:53.0274001Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:54:53.0274117Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T04:54:53.0274196Z         
2025-03-14T04:54:53.0274502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:54:53.0274662Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:54:53.0274777Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T04:54:53.0274838Z         
2025-03-14T04:54:53.0275142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:54:53.0275328Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T04:54:53.0275433Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T04:54:53.0275503Z         
2025-03-14T04:54:53.0275833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:54:53.0275974Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T04:54:53.0276035Z         
2025-03-14T04:54:53.0276369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:54:53.0276499Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T04:54:53.0276584Z         
2025-03-14T04:54:53.0276926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:54:53.0277080Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T04:54:53.0277200Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T04:54:53.0277356Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T04:54:53.0277486Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T04:54:53.0277556Z         
2025-03-14T04:54:53.0277898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:54:53.0278036Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T04:54:53.0278151Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T04:54:53.0278307Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T04:54:53.0278435Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T04:54:53.0278506Z         
2025-03-14T04:54:53.0278833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:54:53.0278954Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T04:54:53.0279113Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T04:54:53.0279252Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T04:54:53.0279315Z         
2025-03-14T04:54:53.0279655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:54:53.0279767Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T04:54:53.0279959Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T04:54:53.0280088Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T04:54:53.0280161Z         
2025-03-14T04:54:53.0280485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:54:53.0280593Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T04:54:53.0280708Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T04:54:53.0280779Z         
2025-03-14T04:54:53.0281098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:54:53.0281198Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T04:54:53.0281306Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T04:54:53.0281377Z         
2025-03-14T04:54:53.0281683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:54:53.0281805Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T04:54:53.0281936Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T04:54:53.0282006Z         
2025-03-14T04:54:53.0282340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:54:53.0282463Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T04:54:53.0282607Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T04:54:53.0282678Z         
2025-03-14T04:54:53.0283032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:54:53.0283230Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T04:54:53.0283299Z         
2025-03-14T04:54:53.0283636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:54:53.0283802Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T04:54:53.0283864Z         
2025-03-14T04:54:53.0284260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:54:53.0284434Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T04:54:53.0284504Z         
2025-03-14T04:54:53.0285003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:54:53.0285147Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:54:53.0285210Z         
2025-03-14T04:54:53.0285522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0285665Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T04:54:53.0285736Z         
2025-03-14T04:54:53.0286189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:54:53.0286329Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T04:54:53.0286447Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T04:54:53.0286575Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:54:53.0286638Z         
2025-03-14T04:54:53.0287117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:54:53.0287248Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:54:53.0287488Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T04:54:53.0287552Z         
2025-03-14T04:54:53.0288025Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:54:53.0288193Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:54:53.0288263Z         
2025-03-14T04:54:53.0288577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0288708Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T04:54:53.0288770Z         
2025-03-14T04:54:53.0289223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:54:53.0289369Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T04:54:53.0289485Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T04:54:53.0289604Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T04:54:53.0289676Z         
2025-03-14T04:54:53.0290143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:54:53.0290290Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:54:53.0290541Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T04:54:53.0290609Z         
2025-03-14T04:54:53.0291092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:54:53.0291267Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:54:53.0291338Z         
2025-03-14T04:54:53.0291760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0291917Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T04:54:53.0291989Z         
2025-03-14T04:54:53.0292487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:54:53.0292616Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T04:54:53.0292768Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T04:54:53.0292903Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T04:54:53.0292985Z         
2025-03-14T04:54:53.0293471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:54:53.0293615Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:54:53.0293856Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T04:54:53.0293928Z         
2025-03-14T04:54:53.0294394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:54:53.0294569Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:54:53.0294633Z         
2025-03-14T04:54:53.0294940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0295064Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T04:54:53.0295133Z         
2025-03-14T04:54:53.0295591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:54:53.0295728Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T04:54:53.0295835Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T04:54:53.0295959Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T04:54:53.0296023Z         
2025-03-14T04:54:53.0296494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:54:53.0296633Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:54:53.0296867Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T04:54:53.0296937Z         
2025-03-14T04:54:53.0297395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:54:53.0297566Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:54:53.0297628Z         
2025-03-14T04:54:53.0297934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0298052Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T04:54:53.0298122Z         
2025-03-14T04:54:53.0298546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:54:53.0298661Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T04:54:53.0298762Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T04:54:53.0298898Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T04:54:53.0298957Z         
2025-03-14T04:54:53.0299436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:54:53.0299597Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:54:53.0299833Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T04:54:53.0299894Z         
2025-03-14T04:54:53.0300356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:54:53.0300514Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:54:53.0300584Z         
2025-03-14T04:54:53.0300878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.0301008Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T04:54:53.0301068Z         
2025-03-14T04:54:53.0301372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:54:53.0301746Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T04:54:53.0302081Z         
2025-03-14T04:54:53.0302365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:54:53.0302840Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T04:54:53.0302913Z         
2025-03-14T04:54:53.0303196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:54:53.0303402Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T04:54:53.0303464Z         
2025-03-14T04:54:53.0303860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:54:53.0304001Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T04:54:53.0304073Z         
2025-03-14T04:54:53.0304373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:54:53.0304528Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T04:54:53.0304589Z         
2025-03-14T04:54:53.0304979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:54:53.0305109Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T04:54:53.0305178Z         
2025-03-14T04:54:53.0305664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:54:53.0305824Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T04:54:53.0305956Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:54:53.0306118Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:54:53.0306249Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:54:53.0306318Z         
2025-03-14T04:54:53.0306688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:54:53.0306811Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:54:53.0306873Z         
2025-03-14T04:54:53.0306881Z 
2025-03-14T04:54:53.0306983Z class GraphModule(torch.nn.Module):
2025-03-14T04:54:53.0438053Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T04:54:53.0439108Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:54:53.0439502Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0440017Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0440478Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0440948Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0441404Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0441838Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0442331Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0442799Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0443282Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0443754Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0444194Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0444674Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0445149Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0445637Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0446096Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0446549Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0447021Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0447501Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0447975Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0448427Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0448886Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:54:53.0449474Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0450043Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0450592Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:54:53.0451112Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:54:53.0451663Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0452200Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0452730Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0453164Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0453599Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0454028Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0454465Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0454908Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0455317Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0455732Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0456099Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0456566Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0457011Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0457444Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0457856Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0458236Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0458681Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0459116Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0459542Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0459956Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0460327Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0460898Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0461360Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0461754Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0462133Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0462494Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0462910Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0463326Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0463722Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0464117Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0464481Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0464886Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0465297Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0465694Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0466073Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0466433Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0466841Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0467251Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0467654Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0468054Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0468411Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0468819Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0469236Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0469622Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0470022Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0470391Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:54:53.0470836Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0471260Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0471657Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:54:53.0472050Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:54:53.0472403Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0472819Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0473220Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0473629Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0474033Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0474434Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0474872Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0475293Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0475706Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0476102Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0476472Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0476923Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0477363Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0477780Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0478177Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0478555Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0478979Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0479413Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0479826Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0480221Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0480593Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0481046Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0481498Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0481919Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0482317Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0482699Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0483128Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0483575Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0483983Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0484407Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0484782Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0485207Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0485656Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0486093Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0486496Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0486864Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0487312Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0487741Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0488209Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0488639Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0489029Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0489490Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0489940Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0490371Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0490816Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0491222Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0491751Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0492235Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0492707Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0493152Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0493562Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0494052Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0494527Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0494987Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0495461Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0495900Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0496383Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0496869Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0497333Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0497778Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0498235Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:54:53.0498735Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0499256Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0499658Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:54:53.0500077Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:54:53.0500451Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0500868Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0501278Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0501662Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0502045Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0502389Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0502867Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0503278Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0503662Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0504044Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0504408Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0504842Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0505292Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0505682Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0506079Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0506426Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0506837Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0507246Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0507655Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0508186Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0508562Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0508994Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0509413Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0509860Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0510261Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0510637Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0511080Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0511509Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0511941Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0512339Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0512743Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0513174Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0513602Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0514016Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0514417Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0514792Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0515221Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0515654Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0516059Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0516477Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0516864Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0517291Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0517717Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0518121Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0518528Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0518914Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0519355Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0519799Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0520207Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0520606Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0520976Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0521409Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0521832Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0522241Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0522637Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0523002Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0523473Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0523897Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0524312Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0524708Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0525085Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0525519Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0525955Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0526384Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0526781Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0527159Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0527586Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0528018Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0528429Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0528831Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0529204Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0529635Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0530084Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0530506Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0530910Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0531287Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0531785Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0532264Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0532723Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0533166Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0533571Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0534013Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0534528Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0534968Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0535410Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0535811Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0536281Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0536738Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0537185Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0537662Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0538065Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0538543Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0538999Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0539451Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0539852Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0540226Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0540640Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0541060Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0541447Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0541821Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0542173Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0542586Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0542984Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0543377Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0543752Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0544104Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0544539Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0544950Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0545337Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0545712Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0546067Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0546475Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0546892Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0547291Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0547678Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0548031Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0548435Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0548847Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0549230Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0549610Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0549960Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0550373Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0550816Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0551237Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0551622Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0551973Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0552397Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0552822Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0553249Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0553651Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0554035Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0554468Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0554863Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0555255Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0555629Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0555987Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0556400Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0556818Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0557237Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0557681Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0558068Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0558510Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0558958Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0559377Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0559773Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0560175Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0560743Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0561205Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0561625Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0562045Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0562425Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0562869Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0563310Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0563728Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0564146Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0564562Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0565037Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0565485Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0565918Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0566356Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0566753Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0567246Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0567705Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0568166Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0568591Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0568983Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0569448Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0569896Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0570335Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0570761Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0571150Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0571683Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0572183Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0572639Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0573060Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0573436Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0573875Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0574296Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0574724Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0575131Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0575492Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0575902Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0576314Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0576707Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0577082Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0577438Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0577854Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0578284Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0578709Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0579112Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0579489Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0579916Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0580352Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0580757Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0581175Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0581529Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0581962Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0582369Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0582756Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0583139Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0583490Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0583907Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0584318Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0584714Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0585098Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0585481Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0585896Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0586298Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0586690Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0587064Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0587425Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0587916Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0588339Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0588731Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0589107Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0589468Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0589879Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0590291Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0590685Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0591058Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0591415Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0591852Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0592279Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0592666Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0593048Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0593406Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0593817Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0594242Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0594625Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0595021Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0595370Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0595785Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0596189Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0596571Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0596954Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0597300Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0597711Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0598127Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0598571Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0598980Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0599350Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0599792Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0600219Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0600640Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0601064Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0601449Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0601891Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0602315Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0602734Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0603133Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0603511Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0620705Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0621271Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0621692Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0622179Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0622580Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0623006Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0623416Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0623813Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0624200Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0624583Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0625005Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0625442Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0625838Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0626219Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0626578Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0626999Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0627408Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0627805Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0628185Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0628547Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0629034Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0629452Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0629846Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0630223Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0630582Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0630986Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0631404Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0633918Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0634330Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0634683Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0635103Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0635521Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0635939Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0636318Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0636680Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0637091Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0637530Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0637924Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0638323Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0638690Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0639118Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0639529Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0639928Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0640319Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0640756Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0641190Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0641617Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0642017Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0642422Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0642781Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0643208Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0643621Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0644028Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0644451Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0644816Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0645248Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0645668Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0646075Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0646479Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0646855Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0647331Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0647751Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0648155Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0648543Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0648913Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0649338Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0649760Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0650164Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0650552Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0650942Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0651360Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0651932Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0652378Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0652803Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0653183Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0653620Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0654047Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0654490Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0654888Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0655259Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0655675Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0656105Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0656499Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0656892Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0657261Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0657677Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0658105Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0658489Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0658874Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0659223Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0659634Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0660063Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0660459Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0660977Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0661391Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:54:53.0661826Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0662245Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0662659Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:54:53.0663104Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:54:53.0663461Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0663877Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0664277Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0664703Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0665080Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0665438Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0665839Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0666255Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0666645Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0667038Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0667400Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0667847Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0668266Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0668655Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0669040Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0669400Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:54:53.0669817Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0670218Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0670588Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:54:53.0670962Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:54:53.0671314Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:54:53.0671720Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0672115Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0672492Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:54:53.0672871Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:54:53.0673209Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:54:53.0673639Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:54:53.0674056Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:54:53.0674472Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:54:53.0674860Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:54:53.0675093Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T04:54:53.0675321Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T04:54:53.0675542Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T04:54:53.0675764Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T04:54:53.0675987Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T04:54:53.0676209Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T04:54:53.0676425Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T04:54:53.0676645Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T04:54:53.0676864Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T04:54:53.0677085Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T04:54:53.0677317Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T04:54:53.0677532Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T04:54:53.0677757Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T04:54:53.0677973Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T04:54:53.0678201Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T04:54:53.0678409Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T04:54:53.0678774Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:54:53.0679125Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T04:54:53.0679480Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T04:54:53.0679840Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T04:54:53.0680208Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T04:54:53.0680546Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:54:53.0680864Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:54:53.0681245Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:54:53.0681600Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:54:53.0681968Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:54:53.0682320Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:54:53.0682407Z         
2025-03-14T04:54:53.0682699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0683256Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0683332Z         
2025-03-14T04:54:53.0683617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0685431Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0685500Z         
2025-03-14T04:54:53.0685811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:54:53.0685959Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:54:53.0686033Z         
2025-03-14T04:54:53.0686415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:54:53.0686689Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:54:53.0686778Z         
2025-03-14T04:54:53.0687054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0687595Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0687661Z         
2025-03-14T04:54:53.0687951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0689874Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0689952Z         
2025-03-14T04:54:53.0690265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0690409Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:54:53.0690483Z         
2025-03-14T04:54:53.0690746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0691313Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0691379Z         
2025-03-14T04:54:53.0691765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0693852Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0693925Z         
2025-03-14T04:54:53.0694236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0694403Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:54:53.0694476Z         
2025-03-14T04:54:53.0694758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0695301Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0695372Z         
2025-03-14T04:54:53.0695668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0697582Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0697652Z         
2025-03-14T04:54:53.0697932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0698465Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:54:53.0698557Z         
2025-03-14T04:54:53.0698844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0700764Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0700838Z         
2025-03-14T04:54:53.0701132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0701288Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:54:53.0701371Z         
2025-03-14T04:54:53.0701653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0701826Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:54:53.0701887Z         
2025-03-14T04:54:53.0702151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0702639Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0702707Z         
2025-03-14T04:54:53.0702970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0704782Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0704853Z         
2025-03-14T04:54:53.0705159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0705306Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:54:53.0705367Z         
2025-03-14T04:54:53.0705622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0706117Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0706187Z         
2025-03-14T04:54:53.0706449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0708274Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0708361Z         
2025-03-14T04:54:53.0708667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0708814Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:54:53.0708874Z         
2025-03-14T04:54:53.0709129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0709623Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0709694Z         
2025-03-14T04:54:53.0709955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0711784Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0711870Z         
2025-03-14T04:54:53.0712146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0712306Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:54:53.0712369Z         
2025-03-14T04:54:53.0712659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0712806Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:54:53.0712877Z         
2025-03-14T04:54:53.0713122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0713617Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0713689Z         
2025-03-14T04:54:53.0713949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0715770Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0715849Z         
2025-03-14T04:54:53.0716138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0716280Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:54:53.0716345Z         
2025-03-14T04:54:53.0716616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0717136Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0717209Z         
2025-03-14T04:54:53.0717486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0719383Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0719478Z         
2025-03-14T04:54:53.0719776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0719927Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:54:53.0719993Z         
2025-03-14T04:54:53.0720264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0720783Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0720854Z         
2025-03-14T04:54:53.0721145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0723079Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0723172Z         
2025-03-14T04:54:53.0723464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0723632Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:54:53.0723697Z         
2025-03-14T04:54:53.0724001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0724157Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:54:53.0724231Z         
2025-03-14T04:54:53.0724493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0725014Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0725096Z         
2025-03-14T04:54:53.0725383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0727305Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0727372Z         
2025-03-14T04:54:53.0727676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0727824Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:54:53.0727897Z         
2025-03-14T04:54:53.0728174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0728707Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0728816Z         
2025-03-14T04:54:53.0729094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0730997Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0731074Z         
2025-03-14T04:54:53.0731383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0731634Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:54:53.0731709Z         
2025-03-14T04:54:53.0732000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0732575Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0732677Z         
2025-03-14T04:54:53.0732982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0734864Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0734937Z         
2025-03-14T04:54:53.0735186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0735710Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:54:53.0735828Z         
2025-03-14T04:54:53.0736118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0737980Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0738044Z         
2025-03-14T04:54:53.0738326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0738468Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:54:53.0738537Z         
2025-03-14T04:54:53.0738814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0738967Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:54:53.0739030Z         
2025-03-14T04:54:53.0739282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0739782Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0739854Z         
2025-03-14T04:54:53.0740116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0741946Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0742022Z         
2025-03-14T04:54:53.0742312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0742480Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:54:53.0742543Z         
2025-03-14T04:54:53.0742822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0743312Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0743381Z         
2025-03-14T04:54:53.0743653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0745453Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0745525Z         
2025-03-14T04:54:53.0745814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0745952Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:54:53.0746043Z         
2025-03-14T04:54:53.0746299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0746782Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0746844Z         
2025-03-14T04:54:53.0747104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0748894Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0748971Z         
2025-03-14T04:54:53.0749255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0749454Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:54:53.0749526Z         
2025-03-14T04:54:53.0749810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0749964Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:54:53.0750026Z         
2025-03-14T04:54:53.0750283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0750771Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0750840Z         
2025-03-14T04:54:53.0751113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0752899Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0752982Z         
2025-03-14T04:54:53.0753262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0753407Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:54:53.0753471Z         
2025-03-14T04:54:53.0753728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0754231Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0754297Z         
2025-03-14T04:54:53.0754570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0756401Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0756488Z         
2025-03-14T04:54:53.0756777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0756914Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:54:53.0756983Z         
2025-03-14T04:54:53.0757244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0757785Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0757847Z         
2025-03-14T04:54:53.0758120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0759964Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0760052Z         
2025-03-14T04:54:53.0760341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0760496Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:54:53.0760770Z         
2025-03-14T04:54:53.0761059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0761219Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:54:53.0761280Z         
2025-03-14T04:54:53.0761535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0762072Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0762149Z         
2025-03-14T04:54:53.0762425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0764327Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0764403Z         
2025-03-14T04:54:53.0764708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0764861Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:54:53.0764924Z         
2025-03-14T04:54:53.0765193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0765714Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0765787Z         
2025-03-14T04:54:53.0766074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0767999Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0768071Z         
2025-03-14T04:54:53.0768368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0768517Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:54:53.0768583Z         
2025-03-14T04:54:53.0768849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0769393Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0769474Z         
2025-03-14T04:54:53.0769782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0771761Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0771850Z         
2025-03-14T04:54:53.0772179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0772354Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:54:53.0772432Z         
2025-03-14T04:54:53.0772756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0772940Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:54:53.0773008Z         
2025-03-14T04:54:53.0773292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0773847Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0773921Z         
2025-03-14T04:54:53.0774198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0776091Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0776181Z         
2025-03-14T04:54:53.0776463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0776623Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:54:53.0776685Z         
2025-03-14T04:54:53.0776951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0777446Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0777519Z         
2025-03-14T04:54:53.0777783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0779567Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0779639Z         
2025-03-14T04:54:53.0779919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0780059Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:54:53.0780138Z         
2025-03-14T04:54:53.0780395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0780890Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0780957Z         
2025-03-14T04:54:53.0781226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0783049Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0783121Z         
2025-03-14T04:54:53.0783383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0783903Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:54:53.0783973Z         
2025-03-14T04:54:53.0784239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0786091Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0786166Z         
2025-03-14T04:54:53.0786448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0786597Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:54:53.0786664Z         
2025-03-14T04:54:53.0786960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0787123Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:54:53.0787189Z         
2025-03-14T04:54:53.0787434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0787925Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0787986Z         
2025-03-14T04:54:53.0788253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0790050Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0790135Z         
2025-03-14T04:54:53.0790441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0790577Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:54:53.0790647Z         
2025-03-14T04:54:53.0790893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0791384Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0791446Z         
2025-03-14T04:54:53.0791714Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0793541Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0793625Z         
2025-03-14T04:54:53.0793930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0794064Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:54:53.0794135Z         
2025-03-14T04:54:53.0794393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0794897Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0794960Z         
2025-03-14T04:54:53.0795244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0797172Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0797255Z         
2025-03-14T04:54:53.0797551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0797703Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:54:53.0797773Z         
2025-03-14T04:54:53.0798069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0798220Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:54:53.0798284Z         
2025-03-14T04:54:53.0798557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0799071Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0799135Z         
2025-03-14T04:54:53.0799422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0801318Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0801413Z         
2025-03-14T04:54:53.0801719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0801859Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:54:53.0801932Z         
2025-03-14T04:54:53.0802193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0802721Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0802785Z         
2025-03-14T04:54:53.0803082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0804992Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0805083Z         
2025-03-14T04:54:53.0805392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0805529Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:54:53.0805601Z         
2025-03-14T04:54:53.0805861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0806381Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0806445Z         
2025-03-14T04:54:53.0806732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0808634Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0808715Z         
2025-03-14T04:54:53.0809015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0809169Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:54:53.0809244Z         
2025-03-14T04:54:53.0809536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0809687Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:54:53.0809750Z         
2025-03-14T04:54:53.0810054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0810592Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0810688Z         
2025-03-14T04:54:53.0811003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0813261Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0813346Z         
2025-03-14T04:54:53.0813662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0813812Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:54:53.0813878Z         
2025-03-14T04:54:53.0814163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0814714Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0814807Z         
2025-03-14T04:54:53.0815111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0817100Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0817180Z         
2025-03-14T04:54:53.0817501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0817662Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:54:53.0817736Z         
2025-03-14T04:54:53.0818010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0818602Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0818672Z         
2025-03-14T04:54:53.0818973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0820975Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0821054Z         
2025-03-14T04:54:53.0821354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0821495Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:54:53.0821567Z         
2025-03-14T04:54:53.0821842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0821988Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:54:53.0822063Z         
2025-03-14T04:54:53.0822323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0822798Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0822872Z         
2025-03-14T04:54:53.0823136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0824954Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0825038Z         
2025-03-14T04:54:53.0825319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0825474Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:54:53.0825534Z         
2025-03-14T04:54:53.0825789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0826275Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0826340Z         
2025-03-14T04:54:53.0826601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0828414Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0828484Z         
2025-03-14T04:54:53.0828765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0828920Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:54:53.0828978Z         
2025-03-14T04:54:53.0829232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0829729Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0829791Z         
2025-03-14T04:54:53.0830062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0831880Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0831967Z         
2025-03-14T04:54:53.0832269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0832413Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:54:53.0832480Z         
2025-03-14T04:54:53.0832761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0832915Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:54:53.0832977Z         
2025-03-14T04:54:53.0833246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0833745Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0833814Z         
2025-03-14T04:54:53.0834075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0835963Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0836050Z         
2025-03-14T04:54:53.0836351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0836504Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:54:53.0836565Z         
2025-03-14T04:54:53.0836821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0837309Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0837380Z         
2025-03-14T04:54:53.0837643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0839488Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0839576Z         
2025-03-14T04:54:53.0839874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0840016Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:54:53.0840081Z         
2025-03-14T04:54:53.0840347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0840831Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0840902Z         
2025-03-14T04:54:53.0841163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0842951Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0843038Z         
2025-03-14T04:54:53.0843327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0843484Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:54:53.0843547Z         
2025-03-14T04:54:53.0843855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0843992Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:54:53.0844060Z         
2025-03-14T04:54:53.0844303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0844829Z         x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0844913Z         
2025-03-14T04:54:53.0845192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0847089Z         x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0847166Z         
2025-03-14T04:54:53.0847464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0847606Z         out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:54:53.0847669Z         
2025-03-14T04:54:53.0847939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0848448Z         x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0848520Z         
2025-03-14T04:54:53.0848795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0850717Z         x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0850795Z         
2025-03-14T04:54:53.0851091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0851229Z         out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:54:53.0851292Z         
2025-03-14T04:54:53.0851653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0852173Z         x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0852267Z         
2025-03-14T04:54:53.0852560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0854475Z         x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0854557Z         
2025-03-14T04:54:53.0854859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0855021Z         x_93 += out_51;  out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:54:53.0855089Z         
2025-03-14T04:54:53.0855400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0855548Z         out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:54:53.0855626Z         
2025-03-14T04:54:53.0855892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0856421Z         x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0856484Z         
2025-03-14T04:54:53.0856772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0858671Z         x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0858738Z         
2025-03-14T04:54:53.0859044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0859195Z         out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:54:53.0859270Z         
2025-03-14T04:54:53.0859553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0860076Z         x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0860148Z         
2025-03-14T04:54:53.0860431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0862376Z         x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0862453Z         
2025-03-14T04:54:53.0862738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0862874Z         out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:54:53.0862970Z         
2025-03-14T04:54:53.0863228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0863711Z         x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0863782Z         
2025-03-14T04:54:53.0864041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0865857Z         x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0865951Z         
2025-03-14T04:54:53.0866232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0866406Z         x_99 += out_55;  out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:54:53.0866469Z         
2025-03-14T04:54:53.0866758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0866896Z         out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:54:53.0866965Z         
2025-03-14T04:54:53.0867213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0867714Z         x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0867777Z         
2025-03-14T04:54:53.0868046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0869863Z         x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0870187Z         
2025-03-14T04:54:53.0870478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0870617Z         out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:54:53.0870689Z         
2025-03-14T04:54:53.0870936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0871436Z         x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0871500Z         
2025-03-14T04:54:53.0871777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0873607Z         x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0873686Z         
2025-03-14T04:54:53.0873978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0874116Z         out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:54:53.0874184Z         
2025-03-14T04:54:53.0874433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0874936Z         x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0875004Z         
2025-03-14T04:54:53.0875268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0877084Z         x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0877171Z         
2025-03-14T04:54:53.0877446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0877605Z         x_105 += out_59;  out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:54:53.0877668Z         
2025-03-14T04:54:53.0877952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0878093Z         out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:54:53.0878162Z         
2025-03-14T04:54:53.0878407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0878903Z         x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0878965Z         
2025-03-14T04:54:53.0879233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0881094Z         x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0881168Z         
2025-03-14T04:54:53.0881458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0881593Z         out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:54:53.0881661Z         
2025-03-14T04:54:53.0881905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0882396Z         x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0882456Z         
2025-03-14T04:54:53.0882723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0884546Z         x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0884611Z         
2025-03-14T04:54:53.0884902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0885035Z         out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:54:53.0885102Z         
2025-03-14T04:54:53.0885347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0885877Z         x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0885957Z         
2025-03-14T04:54:53.0886238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0888126Z         x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0888194Z         
2025-03-14T04:54:53.0888492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0888648Z         x_111 += out_63;  out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:54:53.0888718Z         
2025-03-14T04:54:53.0889008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0889158Z         out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:54:53.0889222Z         
2025-03-14T04:54:53.0889493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0890037Z         x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0890102Z         
2025-03-14T04:54:53.0890383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0892530Z         x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0892616Z         
2025-03-14T04:54:53.0892964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0893129Z         out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:54:53.0893220Z         
2025-03-14T04:54:53.0893482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0894025Z         x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0894090Z         
2025-03-14T04:54:53.0894381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0896300Z         x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0896376Z         
2025-03-14T04:54:53.0896688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0896829Z         out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:54:53.0896917Z         
2025-03-14T04:54:53.0897178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0897704Z         x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0897767Z         
2025-03-14T04:54:53.0898053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0899984Z         x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0900053Z         
2025-03-14T04:54:53.0900354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0900526Z         x_117 += out_67;  out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:54:53.0900614Z         
2025-03-14T04:54:53.0900908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0901064Z         out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:54:53.0901128Z         
2025-03-14T04:54:53.0901397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0901913Z         x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0901984Z         
2025-03-14T04:54:53.0902254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0904048Z         x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0904135Z         
2025-03-14T04:54:53.0904421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0904562Z         out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:54:53.0904629Z         
2025-03-14T04:54:53.0904881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0905380Z         x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0905443Z         
2025-03-14T04:54:53.0905717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0907564Z         x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0907659Z         
2025-03-14T04:54:53.0907964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0908105Z         out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:54:53.0908176Z         
2025-03-14T04:54:53.0908435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0908959Z         x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0909026Z         
2025-03-14T04:54:53.0909308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0911218Z         x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0911303Z         
2025-03-14T04:54:53.0911603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0911759Z         x_123 += out_71;  out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:54:53.0911832Z         
2025-03-14T04:54:53.0912125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0912280Z         out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:54:53.0912347Z         
2025-03-14T04:54:53.0912616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0913140Z         x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0913212Z         
2025-03-14T04:54:53.0913493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0915446Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0915524Z         
2025-03-14T04:54:53.0915820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0915969Z         out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:54:53.0916033Z         
2025-03-14T04:54:53.0916302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0916824Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0916894Z         
2025-03-14T04:54:53.0917176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0918991Z         x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0919077Z         
2025-03-14T04:54:53.0919368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0919498Z         out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:54:53.0919567Z         
2025-03-14T04:54:53.0919812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0920320Z         x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0920395Z         
2025-03-14T04:54:53.0920668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0922546Z         x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0922619Z         
2025-03-14T04:54:53.0922904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0923049Z         x_129 += out_75;  out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:54:53.0923119Z         
2025-03-14T04:54:53.0923395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0923540Z         out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:54:53.0923603Z         
2025-03-14T04:54:53.0923858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0924342Z         x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0924432Z         
2025-03-14T04:54:53.0924699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0926573Z         x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0926651Z         
2025-03-14T04:54:53.0926973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0927123Z         out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:54:53.0927207Z         
2025-03-14T04:54:53.0927475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0928007Z         x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0928081Z         
2025-03-14T04:54:53.0928359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0930331Z         x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0930412Z         
2025-03-14T04:54:53.0930727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0930881Z         out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:54:53.0930947Z         
2025-03-14T04:54:53.0931246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0931868Z         x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0931944Z         
2025-03-14T04:54:53.0932259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0934210Z         x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0934289Z         
2025-03-14T04:54:53.0934587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0934763Z         x_135 += out_79;  out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:54:53.0934840Z         
2025-03-14T04:54:53.0935153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0935311Z         out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:54:53.0935376Z         
2025-03-14T04:54:53.0935640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0936149Z         x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0936222Z         
2025-03-14T04:54:53.0936496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0938405Z         x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0938499Z         
2025-03-14T04:54:53.0938794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0938936Z         out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:54:53.0939000Z         
2025-03-14T04:54:53.0939267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0939781Z         x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0939857Z         
2025-03-14T04:54:53.0940136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0941977Z         x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0942070Z         
2025-03-14T04:54:53.0942349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0942485Z         out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:54:53.0942548Z         
2025-03-14T04:54:53.0942804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0943294Z         x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0943363Z         
2025-03-14T04:54:53.0943627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0945477Z         x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0945563Z         
2025-03-14T04:54:53.0945844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0946003Z         x_141 += out_83;  out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:54:53.0946064Z         
2025-03-14T04:54:53.0946353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0946492Z         out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:54:53.0946562Z         
2025-03-14T04:54:53.0946811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0947302Z         x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0947391Z         
2025-03-14T04:54:53.0947664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0949534Z         x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0949606Z         
2025-03-14T04:54:53.0949888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0950029Z         out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:54:53.0950089Z         
2025-03-14T04:54:53.0950344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0950831Z         x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0950900Z         
2025-03-14T04:54:53.0951166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0952976Z         x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0953095Z         
2025-03-14T04:54:53.0953383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0953520Z         out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:54:53.0953580Z         
2025-03-14T04:54:53.0953831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0954331Z         x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0954413Z         
2025-03-14T04:54:53.0954672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0956513Z         x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0956587Z         
2025-03-14T04:54:53.0956869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0957024Z         x_147 += out_87;  out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:54:53.0957085Z         
2025-03-14T04:54:53.0957376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0957522Z         out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:54:53.0957596Z         
2025-03-14T04:54:53.0957856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0958373Z         x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0958464Z         
2025-03-14T04:54:53.0958740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0960762Z         x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0960836Z         
2025-03-14T04:54:53.0961221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0961379Z         out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:54:53.0961469Z         
2025-03-14T04:54:53.0961758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0962364Z         x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0962443Z         
2025-03-14T04:54:53.0962739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0964770Z         x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0964852Z         
2025-03-14T04:54:53.0965173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0965329Z         out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:54:53.0965398Z         
2025-03-14T04:54:53.0965710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0966255Z         x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0966330Z         
2025-03-14T04:54:53.0966623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0968715Z         x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0968796Z         
2025-03-14T04:54:53.0969117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0969310Z         x_153 += out_91;  out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:54:53.0969380Z         
2025-03-14T04:54:53.0969732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0969890Z         out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:54:53.0969967Z         
2025-03-14T04:54:53.0970248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0970812Z         x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0970880Z         
2025-03-14T04:54:53.0971182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0973376Z         x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0973473Z         
2025-03-14T04:54:53.0973806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0973942Z         out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:54:53.0974017Z         
2025-03-14T04:54:53.0974277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0974797Z         x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0974874Z         
2025-03-14T04:54:53.0975150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0977115Z         x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0977206Z         
2025-03-14T04:54:53.0977503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0977647Z         out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:54:53.0977714Z         
2025-03-14T04:54:53.0977981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0978491Z         x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0978568Z         
2025-03-14T04:54:53.0978844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0980718Z         x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0980803Z         
2025-03-14T04:54:53.0981082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0981237Z         x_159 += out_95;  out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:54:53.0981298Z         
2025-03-14T04:54:53.0981592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0981735Z         out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:54:53.0981807Z         
2025-03-14T04:54:53.0982067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0982579Z         x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0982642Z         
2025-03-14T04:54:53.0982941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0984832Z         x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0984911Z         
2025-03-14T04:54:53.0985198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0985340Z         out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:54:53.0985407Z         
2025-03-14T04:54:53.0985652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0986157Z         x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0986218Z         
2025-03-14T04:54:53.0986487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0988289Z         x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0988371Z         
2025-03-14T04:54:53.0988682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0988828Z         out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:54:53.0988899Z         
2025-03-14T04:54:53.0989159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0989708Z         x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.0989779Z         
2025-03-14T04:54:53.0990055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0991893Z         x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0991970Z         
2025-03-14T04:54:53.0992263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.0992431Z         x_165 += out_99;  out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:54:53.0992497Z         
2025-03-14T04:54:53.0992804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0992961Z         out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:54:53.0993032Z         
2025-03-14T04:54:53.0993293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0993819Z         x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.0993912Z         
2025-03-14T04:54:53.0994198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0996112Z         x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0996186Z         
2025-03-14T04:54:53.0996510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.0996651Z         out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:54:53.0996746Z         
2025-03-14T04:54:53.0997008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.0997554Z         x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.0997621Z         
2025-03-14T04:54:53.0997906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.0999812Z         x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.0999878Z         
2025-03-14T04:54:53.1000183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1000323Z         out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:54:53.1000395Z         
2025-03-14T04:54:53.1000651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1001220Z         x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.1001287Z         
2025-03-14T04:54:53.1001590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1003636Z         x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1003710Z         
2025-03-14T04:54:53.1004028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.1004223Z         x_171 += out_103;  out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:54:53.1004302Z         
2025-03-14T04:54:53.1004632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1004805Z         out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:54:53.1004872Z         
2025-03-14T04:54:53.1005159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1005704Z         x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.1005773Z         
2025-03-14T04:54:53.1006077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1008106Z         x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1008206Z         
2025-03-14T04:54:53.1008534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1008680Z         out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:54:53.1008755Z         
2025-03-14T04:54:53.1009034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1009602Z         x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.1009673Z         
2025-03-14T04:54:53.1009981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1012156Z         x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1012259Z         
2025-03-14T04:54:53.1012593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1012752Z         out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:54:53.1012829Z         
2025-03-14T04:54:53.1013102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1013662Z         x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.1013734Z         
2025-03-14T04:54:53.1014037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1016051Z         x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1016139Z         
2025-03-14T04:54:53.1016459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.1016635Z         x_177 += out_107;  out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:54:53.1016714Z         
2025-03-14T04:54:53.1017028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1017198Z         out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:54:53.1017267Z         
2025-03-14T04:54:53.1017552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1018101Z         x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.1018194Z         
2025-03-14T04:54:53.1018496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1020559Z         x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1020663Z         
2025-03-14T04:54:53.1020977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1021135Z         out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:54:53.1021208Z         
2025-03-14T04:54:53.1021483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1022032Z         x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.1022097Z         
2025-03-14T04:54:53.1022369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1024226Z         x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1024324Z         
2025-03-14T04:54:53.1024635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1024775Z         out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:54:53.1024847Z         
2025-03-14T04:54:53.1025108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1025660Z         x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.1025736Z         
2025-03-14T04:54:53.1026005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1027859Z         x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1027921Z         
2025-03-14T04:54:53.1028208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.1028362Z         x_183 += out_111;  out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:54:53.1028429Z         
2025-03-14T04:54:53.1028712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1028864Z         out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:54:53.1028924Z         
2025-03-14T04:54:53.1029180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1029663Z         x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.1029749Z         
2025-03-14T04:54:53.1030010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1031823Z         x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1031896Z         
2025-03-14T04:54:53.1032188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1032332Z         out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:54:53.1032408Z         
2025-03-14T04:54:53.1032674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1033212Z         x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.1033285Z         
2025-03-14T04:54:53.1033567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1035446Z         x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1035517Z         
2025-03-14T04:54:53.1035804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1035937Z         out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:54:53.1036005Z         
2025-03-14T04:54:53.1036266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1036772Z         x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.1036834Z         
2025-03-14T04:54:53.1037101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1038929Z         x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1039003Z         
2025-03-14T04:54:53.1039289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.1039456Z         x_189 += out_115;  out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:54:53.1039527Z         
2025-03-14T04:54:53.1039820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1039971Z         out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:54:53.1040033Z         
2025-03-14T04:54:53.1040286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1040767Z         x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.1040838Z         
2025-03-14T04:54:53.1041100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1042895Z         x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1042985Z         
2025-03-14T04:54:53.1043268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1043411Z         out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191);  x_191 = None
2025-03-14T04:54:53.1043472Z         
2025-03-14T04:54:53.1043726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1044215Z         x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.1044285Z         
2025-03-14T04:54:53.1044547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1046407Z         x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1046498Z         
2025-03-14T04:54:53.1046783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1046921Z         out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193);  x_193 = None
2025-03-14T04:54:53.1046983Z         
2025-03-14T04:54:53.1047240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1047752Z         x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.1047820Z         
2025-03-14T04:54:53.1048106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1050003Z         x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1050097Z         
2025-03-14T04:54:53.1050368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1050894Z         x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:54:53.1050967Z         
2025-03-14T04:54:53.1051249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1053462Z         x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1053557Z         
2025-03-14T04:54:53.1053849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.1054014Z         x_195 += x_197;  out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195;  x_195 = x_197 = None
2025-03-14T04:54:53.1054078Z         
2025-03-14T04:54:53.1054380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1054528Z         out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122);  out_122 = None
2025-03-14T04:54:53.1054603Z         
2025-03-14T04:54:53.1054865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1055378Z         x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.1055444Z         
2025-03-14T04:54:53.1055770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1057668Z         x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1057753Z         
2025-03-14T04:54:53.1058064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1058206Z         out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199);  x_199 = None
2025-03-14T04:54:53.1058280Z         
2025-03-14T04:54:53.1058547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1059073Z         x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.1059138Z         
2025-03-14T04:54:53.1059438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1061347Z         x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1061478Z         
2025-03-14T04:54:53.1061770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1061907Z         out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201);  x_201 = None
2025-03-14T04:54:53.1061982Z         
2025-03-14T04:54:53.1062229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1062730Z         x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.1062802Z         
2025-03-14T04:54:53.1063064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1064906Z         x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1065000Z         
2025-03-14T04:54:53.1065278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.1065441Z         x_203 += out_123;  out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203;  x_203 = out_123 = None
2025-03-14T04:54:53.1065503Z         
2025-03-14T04:54:53.1065788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1065931Z         out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126);  out_126 = None
2025-03-14T04:54:53.1065998Z         
2025-03-14T04:54:53.1066267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1066758Z         x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:54:53.1066837Z         
2025-03-14T04:54:53.1067124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1068918Z         x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1068992Z         
2025-03-14T04:54:53.1069281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1069415Z         out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205);  x_205 = None
2025-03-14T04:54:53.1069483Z         
2025-03-14T04:54:53.1069730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1070223Z         x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:54:53.1070303Z         
2025-03-14T04:54:53.1070575Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1072367Z         x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1072433Z         
2025-03-14T04:54:53.1072723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1072876Z         out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207);  x_207 = None
2025-03-14T04:54:53.1072946Z         
2025-03-14T04:54:53.1073195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1073722Z         x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:54:53.1073786Z         
2025-03-14T04:54:53.1074058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:54:53.1075874Z         x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:54:53.1075939Z         
2025-03-14T04:54:53.1076223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:54:53.1076378Z         x_209 += out_127;  out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209;  x_209 = out_127 = None
2025-03-14T04:54:53.1076447Z         
2025-03-14T04:54:53.1076728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:54:53.1076877Z         out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130);  out_130 = None
2025-03-14T04:54:53.1076957Z         
2025-03-14T04:54:53.1077217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1077831Z         x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T04:54:53.1077895Z         
2025-03-14T04:54:53.1078163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1078745Z         x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T04:54:53.1078817Z         
2025-03-14T04:54:53.1079250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:54:53.1079565Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest');  x_210 = None
2025-03-14T04:54:53.1079627Z         
2025-03-14T04:54:53.1079880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1080520Z         x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T04:54:53.1080594Z         
2025-03-14T04:54:53.1080956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:54:53.1081163Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features;  x_212 = top_down_features = None
2025-03-14T04:54:53.1081232Z         
2025-03-14T04:54:53.1081487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1082086Z         x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T04:54:53.1082151Z         
2025-03-14T04:54:53.1082587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:54:53.1082906Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T04:54:53.1082976Z         
2025-03-14T04:54:53.1083221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1083802Z         x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T04:54:53.1083880Z         
2025-03-14T04:54:53.1084233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:54:53.1084446Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1;  x_214 = top_down_features_1 = None
2025-03-14T04:54:53.1084514Z         
2025-03-14T04:54:53.1084764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1085351Z         x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T04:54:53.1085421Z         
2025-03-14T04:54:53.1085822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:54:53.1086164Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T04:54:53.1086239Z         
2025-03-14T04:54:53.1086506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1087135Z         x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T04:54:53.1087208Z         
2025-03-14T04:54:53.1087566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:54:53.1087795Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2;  x_216 = top_down_features_2 = None
2025-03-14T04:54:53.1087859Z         
2025-03-14T04:54:53.1088132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1088790Z         x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T04:54:53.1088854Z         
2025-03-14T04:54:53.1089239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T04:54:53.1089457Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T04:54:53.1089528Z         
2025-03-14T04:54:53.1089998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.1090180Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T04:54:53.1090244Z         
2025-03-14T04:54:53.1090560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1090705Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:54:53.1090777Z         
2025-03-14T04:54:53.1091246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.1091577Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T04:54:53.1091692Z         
2025-03-14T04:54:53.1092051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1092213Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:54:53.1092293Z         
2025-03-14T04:54:53.1092727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:54:53.1092942Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:54:53.1093043Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T04:54:53.1093172Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:54:53.1093256Z         
2025-03-14T04:54:53.1093596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:54:53.1093736Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:54:53.1093807Z         
2025-03-14T04:54:53.1094137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:54:53.1094264Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:54:53.1094326Z         
2025-03-14T04:54:53.1094720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:54:53.1094936Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:54:53.1095005Z         
2025-03-14T04:54:53.1095442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:54:53.1095568Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:54:53.1096008Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:54:53.1096130Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:54:53.1096255Z         x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T04:54:53.1096316Z         
2025-03-14T04:54:53.1096768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.1096938Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T04:54:53.1097008Z         
2025-03-14T04:54:53.1097298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1097444Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:54:53.1097503Z         
2025-03-14T04:54:53.1097940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.1098086Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T04:54:53.1098157Z         
2025-03-14T04:54:53.1098449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1098593Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T04:54:53.1098652Z         
2025-03-14T04:54:53.1099031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:54:53.1099242Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T04:54:53.1099353Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T04:54:53.1099475Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T04:54:53.1099560Z         
2025-03-14T04:54:53.1099900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:54:53.1100037Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T04:54:53.1100097Z         
2025-03-14T04:54:53.1100426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:54:53.1100552Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T04:54:53.1100621Z         
2025-03-14T04:54:53.1101021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:54:53.1101252Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T04:54:53.1101317Z         
2025-03-14T04:54:53.1101760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:54:53.1101901Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T04:54:53.1102341Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T04:54:53.1102479Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T04:54:53.1102601Z         x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T04:54:53.1102673Z         
2025-03-14T04:54:53.1103124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.1103303Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T04:54:53.1103367Z         
2025-03-14T04:54:53.1103682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1103826Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T04:54:53.1103896Z         
2025-03-14T04:54:53.1104353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.1104510Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T04:54:53.1104574Z         
2025-03-14T04:54:53.1104888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1105032Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T04:54:53.1105103Z         
2025-03-14T04:54:53.1105510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:54:53.1105735Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T04:54:53.1105838Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T04:54:53.1105988Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T04:54:53.1106052Z         
2025-03-14T04:54:53.1106417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:54:53.1106546Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T04:54:53.1106618Z         
2025-03-14T04:54:53.1106961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:54:53.1107094Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T04:54:53.1107158Z         
2025-03-14T04:54:53.1107565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:54:53.1107789Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T04:54:53.1107861Z         
2025-03-14T04:54:53.1108303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:54:53.1108435Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T04:54:53.1108881Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T04:54:53.1109007Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T04:54:53.1109134Z         x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T04:54:53.1109200Z         
2025-03-14T04:54:53.1109677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.1109827Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T04:54:53.1109898Z         
2025-03-14T04:54:53.1110201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1110351Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T04:54:53.1110415Z         
2025-03-14T04:54:53.1110872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.1111025Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T04:54:53.1111097Z         
2025-03-14T04:54:53.1111403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1111549Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T04:54:53.1111612Z         
2025-03-14T04:54:53.1112027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:54:53.1112226Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T04:54:53.1112356Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T04:54:53.1112477Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T04:54:53.1112551Z         
2025-03-14T04:54:53.1112907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:54:53.1113042Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T04:54:53.1113107Z         
2025-03-14T04:54:53.1113453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:54:53.1113577Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T04:54:53.1113649Z         
2025-03-14T04:54:53.1114039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:54:53.1114268Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T04:54:53.1114332Z         
2025-03-14T04:54:53.1114767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:54:53.1114901Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T04:54:53.1115337Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T04:54:53.1115471Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T04:54:53.1115591Z         x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T04:54:53.1115685Z         
2025-03-14T04:54:53.1116163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.1116327Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:54:53.1116395Z         
2025-03-14T04:54:53.1116727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1116875Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T04:54:53.1116960Z         
2025-03-14T04:54:53.1117404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:54:53.1117561Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:54:53.1117624Z         
2025-03-14T04:54:53.1117932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1118069Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T04:54:53.1118141Z         
2025-03-14T04:54:53.1118538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:54:53.1118750Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T04:54:53.1118868Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T04:54:53.1118998Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T04:54:53.1119062Z         
2025-03-14T04:54:53.1119434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:54:53.1119560Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T04:54:53.1119635Z         
2025-03-14T04:54:53.1119982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:54:53.1120114Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T04:54:53.1120178Z         
2025-03-14T04:54:53.1120584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:54:53.1120806Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T04:54:53.1120880Z         
2025-03-14T04:54:53.1121318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:54:53.1121455Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T04:54:53.1121914Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T04:54:53.1122045Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T04:54:53.1122195Z         x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T04:54:53.1122266Z         
2025-03-14T04:54:53.1122610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:54:53.1122758Z         tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32);  x_218 = None
2025-03-14T04:54:53.1122909Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32);  x_219 = None
2025-03-14T04:54:53.1123044Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32);  x_220 = None
2025-03-14T04:54:53.1123186Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32);  x_221 = None
2025-03-14T04:54:53.1123316Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32);  x_222 = None
2025-03-14T04:54:53.1123393Z         
2025-03-14T04:54:53.1123684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1124261Z         x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_217 = None
2025-03-14T04:54:53.1124331Z         
2025-03-14T04:54:53.1124642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:54:53.1124884Z         x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False);  x_223 = None
2025-03-14T04:54:53.1124962Z         
2025-03-14T04:54:53.1125387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:54:53.1126017Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:54:53.1126087Z         
2025-03-14T04:54:53.1126494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:54:53.1127090Z         x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_224 = None
2025-03-14T04:54:53.1127159Z         
2025-03-14T04:54:53.1127452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1127999Z         x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_215 = None
2025-03-14T04:54:53.1128076Z         
2025-03-14T04:54:53.1128380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:54:53.1128603Z         x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False);  x_225 = None
2025-03-14T04:54:53.1128674Z         
2025-03-14T04:54:53.1129100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:54:53.1129709Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:54:53.1129784Z         
2025-03-14T04:54:53.1130177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:54:53.1130758Z         x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_226 = None
2025-03-14T04:54:53.1130837Z         
2025-03-14T04:54:53.1131116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1131777Z         x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_213 = None
2025-03-14T04:54:53.1131856Z         
2025-03-14T04:54:53.1132174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:54:53.1132407Z         x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False);  x_227 = None
2025-03-14T04:54:53.1132487Z         
2025-03-14T04:54:53.1132948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:54:53.1133631Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:54:53.1133704Z         
2025-03-14T04:54:53.1134111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:54:53.1134672Z         x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_228 = None
2025-03-14T04:54:53.1134750Z         
2025-03-14T04:54:53.1135029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1135567Z         x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_211 = None
2025-03-14T04:54:53.1135643Z         
2025-03-14T04:54:53.1135943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:54:53.1136147Z         x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False);  x_229 = None
2025-03-14T04:54:53.1136215Z         
2025-03-14T04:54:53.1136638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:54:53.1137221Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:54:53.1137297Z         
2025-03-14T04:54:53.1137697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:54:53.1138272Z         x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_230 = None
2025-03-14T04:54:53.1138340Z         
2025-03-14T04:54:53.1138634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:54:53.1139484Z         x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:54:53.1139562Z         
2025-03-14T04:54:53.1139844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:54:53.1140031Z         x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False);  x_231 = None
2025-03-14T04:54:53.1140104Z         
2025-03-14T04:54:53.1140485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:54:53.1141355Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:54:53.1141416Z         
2025-03-14T04:54:53.1141774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:54:53.1142603Z         x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:54:53.1142666Z         
2025-03-14T04:54:53.1143013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:54:53.1143179Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:54:53.1143329Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:54:53.1143506Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T04:54:53.1143658Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T04:54:53.1143810Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T04:54:53.1143952Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T04:54:53.1144096Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T04:54:53.1144237Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T04:54:53.1144376Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T04:54:53.1144515Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T04:54:53.1144577Z         
2025-03-14T04:54:53.1145007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:54:53.1145186Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304);  x_233 = None
2025-03-14T04:54:53.1145380Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T04:54:53.1145572Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T04:54:53.1145741Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152);  x_234 = None
2025-03-14T04:54:53.1145939Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T04:54:53.1146131Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T04:54:53.1146292Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76);  x_235 = None
2025-03-14T04:54:53.1146455Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T04:54:53.1146628Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T04:54:53.1146770Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38);  x_236 = None
2025-03-14T04:54:53.1146934Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T04:54:53.1147097Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T04:54:53.1147242Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19);  x_237 = None
2025-03-14T04:54:53.1147400Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T04:54:53.1147569Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T04:54:53.1147631Z         
2025-03-14T04:54:53.1148050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:54:53.1148254Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:54:53.1148324Z         
2025-03-14T04:54:53.1148763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:54:53.1148944Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:54:53.1149088Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:54:53.1149233Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:54:53.1149293Z         
2025-03-14T04:54:53.1149672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:54:53.1149837Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:54:53.1149909Z         
2025-03-14T04:54:53.1150218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:54:53.1150367Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:54:53.1150426Z         
2025-03-14T04:54:53.1150742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:54:53.1150872Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:54:53.1151017Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:54:53.1151170Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:54:53.1151239Z         
2025-03-14T04:54:53.1151566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:54:53.1151696Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:54:53.1151841Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:54:53.1151993Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T04:54:53.1152064Z         
2025-03-14T04:54:53.1152374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:54:53.1152502Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:54:53.1152588Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T04:54:53.1152720Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T04:54:53.1152781Z         
2025-03-14T04:54:53.1153094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:54:53.1153241Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:54:53.1153336Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T04:54:53.1153464Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T04:54:53.1153529Z         
2025-03-14T04:54:53.1153866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:54:53.1154033Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:54:53.1154152Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T04:54:53.1154223Z         
2025-03-14T04:54:53.1154549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:54:53.1154729Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:54:53.1154840Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T04:54:53.1154911Z         
2025-03-14T04:54:53.1155207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:54:53.1155365Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:54:53.1155477Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T04:54:53.1155547Z         
2025-03-14T04:54:53.1155844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:54:53.1156036Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:54:53.1156147Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T04:54:53.1156215Z         
2025-03-14T04:54:53.1156563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:54:53.1156718Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:54:53.1156780Z         
2025-03-14T04:54:53.1157150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:54:53.1157309Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:54:53.1157382Z         
2025-03-14T04:54:53.1157758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:54:53.1157911Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:54:53.1158040Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T04:54:53.1158217Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:54:53.1158364Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T04:54:53.1158427Z         
2025-03-14T04:54:53.1158777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:54:53.1158916Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:54:53.1159049Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T04:54:53.1159197Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:54:53.1159340Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T04:54:53.1159402Z         
2025-03-14T04:54:53.1159739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:54:53.1159855Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:54:53.1160025Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:54:53.1160159Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T04:54:53.1160226Z         
2025-03-14T04:54:53.1160716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:54:53.1160859Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:54:53.1161033Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:54:53.1161184Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T04:54:53.1161249Z         
2025-03-14T04:54:53.1161586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:54:53.1161689Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:54:53.1161822Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:54:53.1161887Z         
2025-03-14T04:54:53.1162222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:54:53.1162330Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:54:53.1162454Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:54:53.1162519Z         
2025-03-14T04:54:53.1162831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:54:53.1162985Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:54:53.1163127Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:54:53.1163259Z         
2025-03-14T04:54:53.1163582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:54:53.1163724Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:54:53.1163870Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:54:53.1163932Z         
2025-03-14T04:54:53.1164281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:54:53.1164464Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:54:53.1164535Z         
2025-03-14T04:54:53.1164862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:54:53.1165034Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T04:54:53.1165097Z         
2025-03-14T04:54:53.1165484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:54:53.1165661Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:54:53.1165731Z         
2025-03-14T04:54:53.1166132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:54:53.1166351Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T04:54:53.1166412Z         
2025-03-14T04:54:53.1166852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:54:53.1167043Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T04:54:53.1167194Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T04:54:53.1167340Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T04:54:53.1167403Z         
2025-03-14T04:54:53.1167789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:54:53.1167960Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T04:54:53.1168031Z         
2025-03-14T04:54:53.1168345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:54:53.1168503Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T04:54:53.1168565Z         
2025-03-14T04:54:53.1168885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:54:53.1169018Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T04:54:53.1169178Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:54:53.1169333Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T04:54:53.1169402Z         
2025-03-14T04:54:53.1169726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:54:53.1169876Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T04:54:53.1170013Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T04:54:53.1170176Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T04:54:53.1170238Z         
2025-03-14T04:54:53.1170557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:54:53.1170683Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:54:53.1170781Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T04:54:53.1170914Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T04:54:53.1170986Z         
2025-03-14T04:54:53.1171301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:54:53.1171549Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T04:54:53.1171653Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T04:54:53.1171795Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T04:54:53.1171865Z         
2025-03-14T04:54:53.1172213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:54:53.1172384Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:54:53.1172519Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T04:54:53.1172590Z         
2025-03-14T04:54:53.1172915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:54:53.1173093Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:54:53.1173230Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T04:54:53.1173293Z         
2025-03-14T04:54:53.1173596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:54:53.1173755Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:54:53.1173866Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T04:54:53.1173937Z         
2025-03-14T04:54:53.1174234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:54:53.1174428Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T04:54:53.1174537Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T04:54:53.1174605Z         
2025-03-14T04:54:53.1174935Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:54:53.1175083Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T04:54:53.1175161Z         
2025-03-14T04:54:53.1175503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:54:53.1175657Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T04:54:53.1175726Z         
2025-03-14T04:54:53.1176083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:54:53.1176227Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T04:54:53.1176351Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T04:54:53.1176514Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T04:54:53.1176654Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T04:54:53.1176722Z         
2025-03-14T04:54:53.1177067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:54:53.1177211Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T04:54:53.1177334Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T04:54:53.1177492Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T04:54:53.1177627Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T04:54:53.1177697Z         
2025-03-14T04:54:53.1178030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:54:53.1178151Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T04:54:53.1178310Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T04:54:53.1178453Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T04:54:53.1178532Z         
2025-03-14T04:54:53.1178869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:54:53.1178983Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T04:54:53.1179156Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T04:54:53.1179288Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T04:54:53.1179358Z         
2025-03-14T04:54:53.1179668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:54:53.1179774Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T04:54:53.1179890Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T04:54:53.1179959Z         
2025-03-14T04:54:53.1180265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:54:53.1180365Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T04:54:53.1180484Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T04:54:53.1180545Z         
2025-03-14T04:54:53.1180870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:54:53.1180985Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T04:54:53.1181124Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T04:54:53.1181204Z         
2025-03-14T04:54:53.1181532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:54:53.1181649Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T04:54:53.1181785Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T04:54:53.1181844Z         
2025-03-14T04:54:53.1182194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:54:53.1182387Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T04:54:53.1182455Z         
2025-03-14T04:54:53.1182781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:54:53.1182954Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T04:54:53.1183014Z         
2025-03-14T04:54:53.1183396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:54:53.1183568Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T04:54:53.1183637Z         
2025-03-14T04:54:53.1184032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:54:53.1184244Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T04:54:53.1184308Z         
2025-03-14T04:54:53.1184761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:54:53.1184911Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T04:54:53.1185068Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T04:54:53.1185205Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T04:54:53.1185273Z         
2025-03-14T04:54:53.1185643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:54:53.1185817Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T04:54:53.1185882Z         
2025-03-14T04:54:53.1186200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:54:53.1186344Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T04:54:53.1186415Z         
2025-03-14T04:54:53.1186722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:54:53.1186870Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T04:54:53.1186991Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:54:53.1187145Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T04:54:53.1187223Z         
2025-03-14T04:54:53.1187548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:54:53.1187692Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T04:54:53.1187813Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T04:54:53.1187966Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T04:54:53.1188026Z         
2025-03-14T04:54:53.1188341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:54:53.1188460Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:54:53.1188555Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T04:54:53.1188685Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T04:54:53.1188754Z         
2025-03-14T04:54:53.1189063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:54:53.1189214Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T04:54:53.1189303Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T04:54:53.1189435Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T04:54:53.1189498Z         
2025-03-14T04:54:53.1189806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:54:53.1189956Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:54:53.1190075Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T04:54:53.1190162Z         
2025-03-14T04:54:53.1190466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:54:53.1190614Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:54:53.1190732Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T04:54:53.1190791Z         
2025-03-14T04:54:53.1191096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:54:53.1191241Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:54:53.1191358Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T04:54:53.1191419Z         
2025-03-14T04:54:53.1191725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:54:53.1191909Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T04:54:53.1192023Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T04:54:53.1192085Z         
2025-03-14T04:54:53.1192433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:54:53.1192588Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T04:54:53.1192658Z         
2025-03-14T04:54:53.1192997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:54:53.1193157Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T04:54:53.1193223Z         
2025-03-14T04:54:53.1193597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:54:53.1193743Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T04:54:53.1193867Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T04:54:53.1194030Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T04:54:53.1194166Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T04:54:53.1194238Z         
2025-03-14T04:54:53.1194590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:54:53.1194750Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T04:54:53.1194869Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T04:54:53.1195026Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T04:54:53.1195158Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T04:54:53.1195231Z         
2025-03-14T04:54:53.1195569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:54:53.1195692Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T04:54:53.1195852Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T04:54:53.1196010Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T04:54:53.1196074Z         
2025-03-14T04:54:53.1196411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:54:53.1196522Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T04:54:53.1196692Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T04:54:53.1196823Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T04:54:53.1196893Z         
2025-03-14T04:54:53.1197201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:54:53.1197308Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T04:54:53.1197424Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T04:54:53.1197494Z         
2025-03-14T04:54:53.1197800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:54:53.1197902Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T04:54:53.1198016Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T04:54:53.1198085Z         
2025-03-14T04:54:53.1198404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:54:53.1198531Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T04:54:53.1198681Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T04:54:53.1198753Z         
2025-03-14T04:54:53.1199076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:54:53.1199199Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T04:54:53.1199330Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T04:54:53.1199402Z         
2025-03-14T04:54:53.1199755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:54:53.1199952Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T04:54:53.1200016Z         
2025-03-14T04:54:53.1200360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:54:53.1200527Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T04:54:53.1200596Z         
2025-03-14T04:54:53.1200981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:54:53.1201161Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T04:54:53.1201232Z         
2025-03-14T04:54:53.1201640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:54:53.1201855Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T04:54:53.1201939Z         
2025-03-14T04:54:53.1202386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:54:53.1202537Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T04:54:53.1202697Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T04:54:53.1202837Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T04:54:53.1202908Z         
2025-03-14T04:54:53.1203286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:54:53.1203463Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T04:54:53.1203526Z         
2025-03-14T04:54:53.1203847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:54:53.1203991Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T04:54:53.1204062Z         
2025-03-14T04:54:53.1204379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:54:53.1204532Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T04:54:53.1204658Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:54:53.1204833Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T04:54:53.1204895Z         
2025-03-14T04:54:53.1205240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:54:53.1205365Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T04:54:53.1205495Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T04:54:53.1205642Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T04:54:53.1205714Z         
2025-03-14T04:54:53.1206029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:54:53.1206159Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:54:53.1206250Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T04:54:53.1206388Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T04:54:53.1206451Z         
2025-03-14T04:54:53.1206773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:54:53.1206919Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T04:54:53.1207030Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T04:54:53.1207156Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T04:54:53.1207224Z         
2025-03-14T04:54:53.1207521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:54:53.1207680Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:54:53.1207789Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T04:54:53.1207873Z         
2025-03-14T04:54:53.1208170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:54:53.1208325Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:54:53.1208442Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T04:54:53.1208501Z         
2025-03-14T04:54:53.1208809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:54:53.1208952Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:54:53.1209068Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T04:54:53.1209130Z         
2025-03-14T04:54:53.1209437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:54:53.1209617Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T04:54:53.1209732Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T04:54:53.1209792Z         
2025-03-14T04:54:53.1210147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:54:53.1210283Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T04:54:53.1210350Z         
2025-03-14T04:54:53.1210696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:54:53.1210860Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T04:54:53.1210923Z         
2025-03-14T04:54:53.1211274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:54:53.1211418Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T04:54:53.1211658Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T04:54:53.1211830Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T04:54:53.1211989Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T04:54:53.1212058Z         
2025-03-14T04:54:53.1212449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:54:53.1212601Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T04:54:53.1212744Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T04:54:53.1212896Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T04:54:53.1213038Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T04:54:53.1213102Z         
2025-03-14T04:54:53.1213448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:54:53.1213562Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T04:54:53.1213733Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T04:54:53.1213892Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T04:54:53.1213973Z         
2025-03-14T04:54:53.1214301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:54:53.1214419Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T04:54:53.1214593Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T04:54:53.1214724Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T04:54:53.1214794Z         
2025-03-14T04:54:53.1215110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:54:53.1215215Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T04:54:53.1215332Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T04:54:53.1215402Z         
2025-03-14T04:54:53.1215721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:54:53.1215821Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T04:54:53.1215933Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T04:54:53.1216019Z         
2025-03-14T04:54:53.1216323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:54:53.1216461Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T04:54:53.1216596Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T04:54:53.1216672Z         
2025-03-14T04:54:53.1216994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:54:53.1217117Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T04:54:53.1217241Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T04:54:53.1217307Z         
2025-03-14T04:54:53.1217648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:54:53.1217839Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T04:54:53.1217903Z         
2025-03-14T04:54:53.1218236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:54:53.1218394Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T04:54:53.1218462Z         
2025-03-14T04:54:53.1218840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:54:53.1219019Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T04:54:53.1219080Z         
2025-03-14T04:54:53.1219480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:54:53.1219682Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T04:54:53.1219768Z         
2025-03-14T04:54:53.1220205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:54:53.1220358Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T04:54:53.1220506Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T04:54:53.1220650Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T04:54:53.1220713Z         
2025-03-14T04:54:53.1221087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:54:53.1221252Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T04:54:53.1221323Z         
2025-03-14T04:54:53.1221631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:54:53.1221778Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T04:54:53.1221848Z         
2025-03-14T04:54:53.1222172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:54:53.1222304Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T04:54:53.1222425Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:54:53.1222592Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T04:54:53.1222654Z         
2025-03-14T04:54:53.1222994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:54:53.1223114Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T04:54:53.1223236Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T04:54:53.1223380Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T04:54:53.1223449Z         
2025-03-14T04:54:53.1223754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:54:53.1223879Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:54:53.1223965Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T04:54:53.1224101Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T04:54:53.1224164Z         
2025-03-14T04:54:53.1224477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:54:53.1224618Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T04:54:53.1224712Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T04:54:53.1224836Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T04:54:53.1224905Z         
2025-03-14T04:54:53.1225201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:54:53.1225360Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:54:53.1225487Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T04:54:53.1225558Z         
2025-03-14T04:54:53.1225852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:54:53.1226004Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:54:53.1226110Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T04:54:53.1226178Z         
2025-03-14T04:54:53.1226473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:54:53.1226627Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:54:53.1226730Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T04:54:53.1226801Z         
2025-03-14T04:54:53.1227100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:54:53.1227285Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T04:54:53.1227390Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T04:54:53.1227459Z         
2025-03-14T04:54:53.1227810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:54:53.1227952Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T04:54:53.1228031Z         
2025-03-14T04:54:53.1228370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:54:53.1228524Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T04:54:53.1228586Z         
2025-03-14T04:54:53.1228931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:54:53.1229061Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T04:54:53.1229189Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T04:54:53.1229339Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T04:54:53.1229480Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T04:54:53.1229542Z         
2025-03-14T04:54:53.1229898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:54:53.1230030Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T04:54:53.1230156Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T04:54:53.1230298Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T04:54:53.1230436Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T04:54:53.1230498Z         
2025-03-14T04:54:53.1230837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:54:53.1230950Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T04:54:53.1231128Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T04:54:53.1231260Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T04:54:53.1231328Z         
2025-03-14T04:54:53.1231653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:54:53.1231770Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T04:54:53.1231932Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T04:54:53.1232066Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T04:54:53.1232127Z         
2025-03-14T04:54:53.1232437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:54:53.1232532Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T04:54:53.1232654Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T04:54:53.1232716Z         
2025-03-14T04:54:53.1233028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:54:53.1233119Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T04:54:53.1233252Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T04:54:53.1233315Z         
2025-03-14T04:54:53.1233623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:54:53.1233758Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T04:54:53.1233893Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T04:54:53.1233956Z         
2025-03-14T04:54:53.1234279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:54:53.1234389Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T04:54:53.1234520Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T04:54:53.1234580Z         
2025-03-14T04:54:53.1234928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:54:53.1235110Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T04:54:53.1235180Z         
2025-03-14T04:54:53.1235507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:54:53.1235670Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T04:54:53.1235731Z         
2025-03-14T04:54:53.1236113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:54:53.1236290Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T04:54:53.1236353Z         
2025-03-14T04:54:53.1236844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:54:53.1236996Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:54:53.1237067Z         
2025-03-14T04:54:53.1237360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1237504Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T04:54:53.1237564Z         
2025-03-14T04:54:53.1238004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:54:53.1238115Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T04:54:53.1238229Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T04:54:53.1238340Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:54:53.1238412Z         
2025-03-14T04:54:53.1238873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:54:53.1239010Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:54:53.1239239Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T04:54:53.1239308Z         
2025-03-14T04:54:53.1239776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:54:53.1239963Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:54:53.1240024Z         
2025-03-14T04:54:53.1240342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1240463Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T04:54:53.1240529Z         
2025-03-14T04:54:53.1240960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:54:53.1241082Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T04:54:53.1241185Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T04:54:53.1241307Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T04:54:53.1241369Z         
2025-03-14T04:54:53.1241836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:54:53.1241964Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:54:53.1242203Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T04:54:53.1242264Z         
2025-03-14T04:54:53.1242734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:54:53.1242909Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:54:53.1242971Z         
2025-03-14T04:54:53.1243274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1243416Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T04:54:53.1243483Z         
2025-03-14T04:54:53.1243904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:54:53.1244023Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T04:54:53.1244127Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T04:54:53.1244246Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T04:54:53.1244307Z         
2025-03-14T04:54:53.1244757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:54:53.1244887Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:54:53.1245124Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T04:54:53.1245186Z         
2025-03-14T04:54:53.1245656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:54:53.1245818Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:54:53.1245905Z         
2025-03-14T04:54:53.1246193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1246343Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T04:54:53.1246407Z         
2025-03-14T04:54:53.1246838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:54:53.1246949Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T04:54:53.1247060Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T04:54:53.1247175Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T04:54:53.1247244Z         
2025-03-14T04:54:53.1247692Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:54:53.1247834Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:54:53.1248064Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T04:54:53.1248133Z         
2025-03-14T04:54:53.1248581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:54:53.1248751Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:54:53.1248837Z         
2025-03-14T04:54:53.1249125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1249255Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T04:54:53.1249335Z         
2025-03-14T04:54:53.1249777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:54:53.1249885Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T04:54:53.1249992Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T04:54:53.1250107Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T04:54:53.1250175Z         
2025-03-14T04:54:53.1250626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:54:53.1250797Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:54:53.1251030Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T04:54:53.1251097Z         
2025-03-14T04:54:53.1251636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:54:53.1251835Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:54:53.1251900Z         
2025-03-14T04:54:53.1252210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:54:53.1252353Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T04:54:53.1252428Z         
2025-03-14T04:54:53.1252729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:54:53.1253128Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T04:54:53.1253192Z         
2025-03-14T04:54:53.1253486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:54:53.1253965Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T04:54:53.1254035Z         
2025-03-14T04:54:53.1254323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:54:53.1254518Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T04:54:53.1254588Z         
2025-03-14T04:54:53.1254971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:54:53.1255116Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T04:54:53.1255177Z         
2025-03-14T04:54:53.1255481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:54:53.1255627Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T04:54:53.1255711Z         
2025-03-14T04:54:53.1256094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:54:53.1256230Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T04:54:53.1256291Z         
2025-03-14T04:54:53.1256773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:54:53.1256905Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T04:54:53.1257029Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:54:53.1257180Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:54:53.1257320Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:54:53.1257380Z         
2025-03-14T04:54:53.1257745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:54:53.1257858Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:54:53.1257927Z         
2025-03-14T04:55:13.6046834Z 
2025-03-14T04:55:13.6049960Z class GraphModule(torch.nn.Module):
2025-03-14T04:55:13.6053916Z     def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T04:55:13.6056635Z         l_features_p2_ = L_features_p2_
2025-03-14T04:55:13.6056897Z         l_features_p3_ = L_features_p3_
2025-03-14T04:55:13.6057128Z         l_features_p4_ = L_features_p4_
2025-03-14T04:55:13.6057337Z         l_features_p5_ = L_features_p5_
2025-03-14T04:55:13.6057554Z         l_features_p6_ = L_features_p6_
2025-03-14T04:55:13.6057956Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:55:13.6058552Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T04:55:13.6059148Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T04:55:13.6059721Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T04:55:13.6060351Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T04:55:13.6062855Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:55:13.6063392Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:55:13.6063948Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:55:13.6064568Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:55:13.6065145Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:55:13.6065736Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:55:13.6066134Z         
2025-03-14T04:55:13.6066726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6067484Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T04:55:13.6067764Z         
2025-03-14T04:55:13.6068335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6068848Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:55:13.6069181Z         
2025-03-14T04:55:13.6069780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6070442Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T04:55:13.6070723Z         
2025-03-14T04:55:13.6071140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6071657Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:55:13.6071928Z         
2025-03-14T04:55:13.6072414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:55:13.6073068Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:55:13.6073419Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T04:55:13.6073715Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:55:13.6073960Z         
2025-03-14T04:55:13.6074381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:55:13.6074903Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:55:13.6075150Z         
2025-03-14T04:55:13.6075581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:55:13.6076108Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:55:13.6076364Z         
2025-03-14T04:55:13.6076852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:55:13.6077586Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:55:13.6077935Z         
2025-03-14T04:55:13.6078463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:55:13.6079086Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:55:13.6079609Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:55:13.6080116Z         add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:55:13.6080426Z         x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:55:13.6080658Z         
2025-03-14T04:55:13.6081193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6081846Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T04:55:13.6082119Z         
2025-03-14T04:55:13.6082527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6083030Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:55:13.6083320Z         
2025-03-14T04:55:13.6083845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6084518Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T04:55:13.6084798Z         
2025-03-14T04:55:13.6085184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6085696Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T04:55:13.6085964Z         
2025-03-14T04:55:13.6086444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:55:13.6087097Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T04:55:13.6087467Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T04:55:13.6087756Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T04:55:13.6088011Z         
2025-03-14T04:55:13.6088441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:55:13.6088981Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T04:55:13.6089240Z         
2025-03-14T04:55:13.6089673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:55:13.6090204Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T04:55:13.6090466Z         
2025-03-14T04:55:13.6090955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:55:13.6091810Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T04:55:13.6092160Z         
2025-03-14T04:55:13.6092718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:55:13.6093390Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T04:55:13.6093931Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T04:55:13.6094446Z         add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T04:55:13.6094763Z         x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4);  add_1 = None
2025-03-14T04:55:13.6095009Z         
2025-03-14T04:55:13.6095563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6096234Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T04:55:13.6096512Z         
2025-03-14T04:55:13.6096942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6097458Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T04:55:13.6097748Z         
2025-03-14T04:55:13.6098296Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6099005Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T04:55:13.6099285Z         
2025-03-14T04:55:13.6099675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6100176Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T04:55:13.6100441Z         
2025-03-14T04:55:13.6100912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:55:13.6101558Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T04:55:13.6101927Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T04:55:13.6102209Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T04:55:13.6102451Z         
2025-03-14T04:55:13.6102874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:55:13.6103388Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T04:55:13.6103632Z         
2025-03-14T04:55:13.6104050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:55:13.6104562Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T04:55:13.6104809Z         
2025-03-14T04:55:13.6105277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:55:13.6105978Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T04:55:13.6106305Z         
2025-03-14T04:55:13.6106805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:55:13.6107402Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T04:55:13.6107903Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T04:55:13.6108430Z         add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T04:55:13.6108739Z         x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4);  add_2 = None
2025-03-14T04:55:13.6108995Z         
2025-03-14T04:55:13.6109520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6110163Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T04:55:13.6110433Z         
2025-03-14T04:55:13.6110834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6111334Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T04:55:13.6111622Z         
2025-03-14T04:55:13.6112157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6112823Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T04:55:13.6113097Z         
2025-03-14T04:55:13.6113489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6113988Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T04:55:13.6114253Z         
2025-03-14T04:55:13.6114726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:55:13.6115364Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T04:55:13.6115722Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T04:55:13.6116008Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T04:55:13.6116254Z         
2025-03-14T04:55:13.6116681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:55:13.6117202Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T04:55:13.6117456Z         
2025-03-14T04:55:13.6117877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:55:13.6118401Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T04:55:13.6118656Z         
2025-03-14T04:55:13.6119130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:55:13.6119812Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T04:55:13.6120145Z         
2025-03-14T04:55:13.6120647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:55:13.6121249Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T04:55:13.6121747Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T04:55:13.6122236Z         add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T04:55:13.6122528Z         x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T04:55:13.6122761Z         
2025-03-14T04:55:13.6123280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6123902Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:55:13.6124166Z         
2025-03-14T04:55:13.6124557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6125037Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T04:55:13.6125312Z         
2025-03-14T04:55:13.6125824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6126478Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:55:13.6126740Z         
2025-03-14T04:55:13.6127116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6127607Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T04:55:13.6127870Z         
2025-03-14T04:55:13.6128319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:55:13.6128934Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T04:55:13.6129285Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T04:55:13.6129561Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T04:55:13.6129804Z         
2025-03-14T04:55:13.6130224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:55:13.6130739Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T04:55:13.6130987Z         
2025-03-14T04:55:13.6131489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:55:13.6132040Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T04:55:13.6132302Z         
2025-03-14T04:55:13.6132791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:55:13.6133467Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T04:55:13.6133785Z         
2025-03-14T04:55:13.6134295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:55:13.6134868Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T04:55:13.6135343Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T04:55:13.6135820Z         add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T04:55:13.6136106Z         x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T04:55:13.6136335Z         
2025-03-14T04:55:13.6136717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:55:13.6137184Z         tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:55:13.6137483Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32);  x_1 = None
2025-03-14T04:55:13.6137776Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32);  x_2 = None
2025-03-14T04:55:13.6138090Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32);  x_3 = None
2025-03-14T04:55:13.6138377Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32);  x_4 = None
2025-03-14T04:55:13.6138640Z         
2025-03-14T04:55:13.6138981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:13.6139739Z         x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p2_ = None
2025-03-14T04:55:13.6140271Z         
2025-03-14T04:55:13.6140626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:55:13.6141146Z         x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False);  x_5 = None
2025-03-14T04:55:13.6141447Z         
2025-03-14T04:55:13.6141908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:55:13.6142745Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:55:13.6143268Z         
2025-03-14T04:55:13.6143707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:55:13.6144522Z         x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_6 = None
2025-03-14T04:55:13.6145040Z         
2025-03-14T04:55:13.6145379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:13.6146095Z         x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p3_ = None
2025-03-14T04:55:13.6146643Z         
2025-03-14T04:55:13.6147000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:55:13.6147513Z         x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False);  x_7 = None
2025-03-14T04:55:13.6147810Z         
2025-03-14T04:55:13.6148269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:55:13.6149107Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:55:13.6149627Z         
2025-03-14T04:55:13.6150067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:55:13.6150884Z         x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_8 = None
2025-03-14T04:55:13.6151406Z         
2025-03-14T04:55:13.6151769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:13.6152481Z         x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p4_ = None
2025-03-14T04:55:13.6153015Z         
2025-03-14T04:55:13.6153409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:55:13.6153919Z         x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False);  x_9 = None
2025-03-14T04:55:13.6154214Z         
2025-03-14T04:55:13.6154673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:55:13.6155495Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:55:13.6156007Z         
2025-03-14T04:55:13.6156451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:55:13.6157255Z         x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_10 = None
2025-03-14T04:55:13.6157763Z         
2025-03-14T04:55:13.6158102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:13.6158807Z         x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p5_ = None
2025-03-14T04:55:13.6159323Z         
2025-03-14T04:55:13.6159684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:55:13.6160216Z         x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False);  x_11 = None
2025-03-14T04:55:13.6160727Z         
2025-03-14T04:55:13.6161200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:55:13.6162024Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:55:13.6162534Z         
2025-03-14T04:55:13.6162973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:55:13.6163780Z         x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_12 = None
2025-03-14T04:55:13.6164285Z         
2025-03-14T04:55:13.6164622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:13.6165545Z         x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:55:13.6166258Z         
2025-03-14T04:55:13.6166607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:55:13.6167123Z         x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False);  x_13 = None
2025-03-14T04:55:13.6167412Z         
2025-03-14T04:55:13.6167871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:55:13.6168938Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:55:13.6169689Z         
2025-03-14T04:55:13.6170141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:55:13.6171187Z         x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:55:13.6171969Z         
2025-03-14T04:55:13.6172405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:55:13.6172975Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:55:13.6173344Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:55:13.6173756Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T04:55:13.6174127Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T04:55:13.6174483Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T04:55:13.6174832Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T04:55:13.6175185Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T04:55:13.6175531Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T04:55:13.6175875Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T04:55:13.6176212Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T04:55:13.6176478Z         
2025-03-14T04:55:13.6176997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:55:13.6177652Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304);  x_15 = None
2025-03-14T04:55:13.6178095Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T04:55:13.6178528Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T04:55:13.6178930Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152);  x_16 = None
2025-03-14T04:55:13.6179353Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T04:55:13.6179790Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T04:55:13.6180177Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76);  x_17 = None
2025-03-14T04:55:13.6180552Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T04:55:13.6180950Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T04:55:13.6181327Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38);  x_18 = None
2025-03-14T04:55:13.6181692Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T04:55:13.6182084Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T04:55:13.6182456Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19);  x_19 = None
2025-03-14T04:55:13.6182815Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T04:55:13.6183200Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T04:55:13.6183491Z         
2025-03-14T04:55:13.6183994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:13.6184665Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:55:13.6184992Z         
2025-03-14T04:55:13.6185517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:13.6186193Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:55:13.6186553Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:55:13.6186907Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:55:13.6187165Z         
2025-03-14T04:55:13.6187621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:13.6188214Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:55:13.6188504Z         
2025-03-14T04:55:13.6188900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:13.6189412Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:55:13.6189673Z         
2025-03-14T04:55:13.6190066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:13.6190558Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:55:13.6190890Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6191232Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:55:13.6191532Z         
2025-03-14T04:55:13.6191937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:13.6192452Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:55:13.6192755Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:55:13.6193086Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T04:55:13.6193366Z         
2025-03-14T04:55:13.6193761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:13.6194250Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6194516Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T04:55:13.6194790Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T04:55:13.6195032Z         
2025-03-14T04:55:13.6195420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:13.6195937Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:55:13.6196229Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T04:55:13.6196507Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T04:55:13.6196762Z         
2025-03-14T04:55:13.6197183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:13.6197689Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:13.6198023Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T04:55:13.6198263Z         
2025-03-14T04:55:13.6198648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:13.6199175Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:13.6199504Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T04:55:13.6199741Z         
2025-03-14T04:55:13.6200122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:13.6200625Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:13.6200949Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T04:55:13.6201181Z         
2025-03-14T04:55:13.6201563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:13.6202097Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:55:13.6202446Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T04:55:13.6202678Z         
2025-03-14T04:55:13.6203096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:13.6203650Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:55:13.6203919Z         
2025-03-14T04:55:13.6204334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:13.6204897Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:55:13.6205159Z         
2025-03-14T04:55:13.6205609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:13.6206153Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:55:13.6206475Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T04:55:13.6206809Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:55:13.6207165Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T04:55:13.6207429Z         
2025-03-14T04:55:13.6207872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:13.6208445Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:55:13.6208785Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T04:55:13.6209142Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:55:13.6209511Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T04:55:13.6209782Z         
2025-03-14T04:55:13.6210228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:13.6210756Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:55:13.6211105Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:55:13.6212760Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T04:55:13.6213151Z         
2025-03-14T04:55:13.6213615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:13.6214158Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:55:13.6214524Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:55:13.6214910Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T04:55:13.6215185Z         
2025-03-14T04:55:13.6215605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:13.6216100Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:55:13.6216395Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:55:13.6216641Z         
2025-03-14T04:55:13.6217063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:13.6217544Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:55:13.6217822Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:55:13.6218066Z         
2025-03-14T04:55:13.6218497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:13.6220067Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:55:13.6220415Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:55:13.6220721Z         
2025-03-14T04:55:13.6221135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:13.6221666Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:55:13.6221982Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:55:13.6222243Z         
2025-03-14T04:55:13.6222701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:13.6223316Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:55:13.6223621Z         
2025-03-14T04:55:13.6224060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:13.6224636Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T04:55:13.6224917Z         
2025-03-14T04:55:13.6225381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:13.6225996Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:55:13.6226287Z         
2025-03-14T04:55:13.6226771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:13.6227438Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T04:55:13.6227772Z         
2025-03-14T04:55:13.6228302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:13.6228991Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T04:55:13.6229361Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T04:55:13.6229722Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T04:55:13.6229992Z         
2025-03-14T04:55:13.6230471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:13.6231064Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T04:55:13.6231353Z         
2025-03-14T04:55:13.6231754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:13.6232289Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T04:55:13.6232574Z         
2025-03-14T04:55:13.6232974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:13.6233481Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T04:55:13.6233820Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6234155Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T04:55:13.6234441Z         
2025-03-14T04:55:13.6234840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:13.6235352Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T04:55:13.6235656Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T04:55:13.6235989Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T04:55:13.6236263Z         
2025-03-14T04:55:13.6236660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:13.6237154Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6237427Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T04:55:13.6237700Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T04:55:13.6237952Z         
2025-03-14T04:55:13.6238347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:13.6238865Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T04:55:13.6239165Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T04:55:13.6239847Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T04:55:13.6240484Z         
2025-03-14T04:55:13.6241217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:13.6242085Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:13.6242593Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T04:55:13.6242974Z         
2025-03-14T04:55:13.6243611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:13.6244461Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:13.6244910Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T04:55:13.6245231Z         
2025-03-14T04:55:13.6245796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:13.6246536Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:13.6246979Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T04:55:13.6247298Z         
2025-03-14T04:55:13.6247859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:13.6248660Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T04:55:13.6249133Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T04:55:13.6249474Z         
2025-03-14T04:55:13.6250125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:13.6251098Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T04:55:13.6251928Z         
2025-03-14T04:55:13.6252408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:13.6253019Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T04:55:13.6253295Z         
2025-03-14T04:55:13.6253782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:13.6254345Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T04:55:13.6254674Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T04:55:13.6255029Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T04:55:13.6260323Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T04:55:13.6261313Z         
2025-03-14T04:55:13.6261774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:13.6262357Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T04:55:13.6262687Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T04:55:13.6263032Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T04:55:13.6263396Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T04:55:13.6263667Z         
2025-03-14T04:55:13.6264093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:13.6264608Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T04:55:13.6264953Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T04:55:13.6265462Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T04:55:13.6265740Z         
2025-03-14T04:55:13.6266178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:13.6266695Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T04:55:13.6267049Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T04:55:13.6267428Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T04:55:13.6267693Z         
2025-03-14T04:55:13.6268109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:13.6268587Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T04:55:13.6268868Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T04:55:13.6269112Z         
2025-03-14T04:55:13.6269519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:13.6269992Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T04:55:13.6270277Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T04:55:13.6270518Z         
2025-03-14T04:55:13.6276456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:13.6277024Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T04:55:13.6277379Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T04:55:13.6277636Z         
2025-03-14T04:55:13.6278064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:13.6278545Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T04:55:13.6278848Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T04:55:13.6279098Z         
2025-03-14T04:55:13.6279526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:13.6280119Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T04:55:13.6280419Z         
2025-03-14T04:55:13.6280880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:13.6281456Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T04:55:13.6281740Z         
2025-03-14T04:55:13.6282207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:13.6282822Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T04:55:13.6283115Z         
2025-03-14T04:55:13.6283622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:13.6284278Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T04:55:13.6284658Z         
2025-03-14T04:55:13.6285165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:13.6285792Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T04:55:13.6286145Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T04:55:13.6286490Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T04:55:13.6286753Z         
2025-03-14T04:55:13.6287208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:13.6287800Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T04:55:13.6288087Z         
2025-03-14T04:55:13.6288477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:13.6288984Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T04:55:13.6289243Z         
2025-03-14T04:55:13.6289632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:13.6290145Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T04:55:13.6290453Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6290812Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T04:55:13.6291084Z         
2025-03-14T04:55:13.6291639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:13.6292264Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T04:55:13.6292579Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T04:55:13.6292924Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T04:55:13.6293197Z         
2025-03-14T04:55:13.6293596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:13.6294088Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6294357Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T04:55:13.6294629Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T04:55:13.6294874Z         
2025-03-14T04:55:13.6295272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:13.6295785Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T04:55:13.6296078Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T04:55:13.6296348Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T04:55:13.6296599Z         
2025-03-14T04:55:13.6297000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:13.6297502Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:13.6297823Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T04:55:13.6298074Z         
2025-03-14T04:55:13.6298509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:13.6299010Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:13.6299331Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T04:55:13.6299564Z         
2025-03-14T04:55:13.6299945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:13.6300441Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:13.6300755Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T04:55:13.6300985Z         
2025-03-14T04:55:13.6301372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:13.6301908Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T04:55:13.6302252Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T04:55:13.6302482Z         
2025-03-14T04:55:13.6302922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:13.6303448Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T04:55:13.6303707Z         
2025-03-14T04:55:13.6304137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:13.6304694Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T04:55:13.6304957Z         
2025-03-14T04:55:13.6305388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:13.6305924Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T04:55:13.6306243Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T04:55:13.6306578Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T04:55:13.6306928Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T04:55:13.6307191Z         
2025-03-14T04:55:13.6307621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:13.6308162Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T04:55:13.6308479Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T04:55:13.6308806Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T04:55:13.6309149Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T04:55:13.6309408Z         
2025-03-14T04:55:13.6309820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:13.6310328Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T04:55:13.6310658Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T04:55:13.6311026Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T04:55:13.6311282Z         
2025-03-14T04:55:13.6311699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:13.6312199Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T04:55:13.6312530Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T04:55:13.6312879Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T04:55:13.6313129Z         
2025-03-14T04:55:13.6313524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:13.6313989Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T04:55:13.6314243Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T04:55:13.6314479Z         
2025-03-14T04:55:13.6314868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:13.6315321Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T04:55:13.6315582Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T04:55:13.6315853Z         
2025-03-14T04:55:13.6316254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:13.6316763Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T04:55:13.6317074Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T04:55:13.6317343Z         
2025-03-14T04:55:13.6317743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:13.6318233Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T04:55:13.6318535Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T04:55:13.6318802Z         
2025-03-14T04:55:13.6319248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:13.6319852Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T04:55:13.6320165Z         
2025-03-14T04:55:13.6320597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:13.6321163Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T04:55:13.6321453Z         
2025-03-14T04:55:13.6321938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:13.6322567Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T04:55:13.6322869Z         
2025-03-14T04:55:13.6323418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:13.6324093Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T04:55:13.6324451Z         
2025-03-14T04:55:13.6324972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:13.6325613Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T04:55:13.6325968Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T04:55:13.6326315Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T04:55:13.6326579Z         
2025-03-14T04:55:13.6327043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:13.6327647Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T04:55:13.6327941Z         
2025-03-14T04:55:13.6328339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:13.6328854Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T04:55:13.6329124Z         
2025-03-14T04:55:13.6329541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:13.6330045Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T04:55:13.6330352Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6330698Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T04:55:13.6330966Z         
2025-03-14T04:55:13.6331398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:13.6332010Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T04:55:13.6332325Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T04:55:13.6332673Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T04:55:13.6332938Z         
2025-03-14T04:55:13.6333337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:13.6333824Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6334093Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T04:55:13.6334362Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T04:55:13.6334611Z         
2025-03-14T04:55:13.6335004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:13.6335510Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T04:55:13.6335804Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T04:55:13.6336080Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T04:55:13.6336328Z         
2025-03-14T04:55:13.6336720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:13.6337222Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:13.6337571Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T04:55:13.6337806Z         
2025-03-14T04:55:13.6338188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:13.6338689Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:13.6339009Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T04:55:13.6339240Z         
2025-03-14T04:55:13.6339622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:13.6340122Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:13.6340441Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T04:55:13.6340673Z         
2025-03-14T04:55:13.6341061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:13.6341597Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T04:55:13.6341938Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T04:55:13.6342170Z         
2025-03-14T04:55:13.6342609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:13.6343131Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T04:55:13.6343399Z         
2025-03-14T04:55:13.6343812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:13.6344347Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T04:55:13.6344603Z         
2025-03-14T04:55:13.6345032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:13.6345568Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T04:55:13.6345886Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T04:55:13.6346222Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T04:55:13.6346570Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T04:55:13.6346828Z         
2025-03-14T04:55:13.6347259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:13.6347798Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T04:55:13.6348108Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T04:55:13.6348438Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T04:55:13.6348779Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T04:55:13.6349032Z         
2025-03-14T04:55:13.6349441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:13.6349945Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T04:55:13.6350290Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T04:55:13.6350638Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T04:55:13.6350890Z         
2025-03-14T04:55:13.6351304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:13.6351800Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T04:55:13.6352136Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T04:55:13.6352492Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T04:55:13.6352753Z         
2025-03-14T04:55:13.6353147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:13.6353629Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T04:55:13.6353889Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T04:55:13.6354137Z         
2025-03-14T04:55:13.6354526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:13.6354995Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T04:55:13.6355277Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T04:55:13.6355521Z         
2025-03-14T04:55:13.6355925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:13.6356433Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T04:55:13.6356757Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T04:55:13.6357029Z         
2025-03-14T04:55:13.6357426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:13.6357916Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T04:55:13.6358221Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T04:55:13.6358477Z         
2025-03-14T04:55:13.6358920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:13.6359526Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T04:55:13.6359851Z         
2025-03-14T04:55:13.6360273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:13.6361069Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T04:55:13.6361382Z         
2025-03-14T04:55:13.6363055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:13.6363707Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T04:55:13.6364006Z         
2025-03-14T04:55:13.6364503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:13.6365247Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T04:55:13.6365572Z         
2025-03-14T04:55:13.6366098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:13.6366960Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T04:55:13.6367320Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T04:55:13.6367668Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T04:55:13.6367932Z         
2025-03-14T04:55:13.6368399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:13.6368999Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T04:55:13.6369287Z         
2025-03-14T04:55:13.6369684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:13.6370204Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T04:55:13.6370468Z         
2025-03-14T04:55:13.6370904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:13.6371475Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T04:55:13.6371900Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6372248Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T04:55:13.6372534Z         
2025-03-14T04:55:13.6373004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:13.6373537Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T04:55:13.6373833Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T04:55:13.6374158Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T04:55:13.6374421Z         
2025-03-14T04:55:13.6374817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:13.6375315Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6375588Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T04:55:13.6375861Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T04:55:13.6376118Z         
2025-03-14T04:55:13.6376520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:13.6377042Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T04:55:13.6377340Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T04:55:13.6377613Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T04:55:13.6377864Z         
2025-03-14T04:55:13.6378267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:13.6378786Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:13.6379138Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T04:55:13.6379380Z         
2025-03-14T04:55:13.6379775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:13.6380298Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:13.6380624Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T04:55:13.6380858Z         
2025-03-14T04:55:13.6381250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:13.6381765Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:13.6382084Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T04:55:13.6382316Z         
2025-03-14T04:55:13.6382714Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:13.6383262Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T04:55:13.6383612Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T04:55:13.6383843Z         
2025-03-14T04:55:13.6384290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:13.6384822Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T04:55:13.6385100Z         
2025-03-14T04:55:13.6385545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:13.6386073Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T04:55:13.6386327Z         
2025-03-14T04:55:13.6386760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:13.6387306Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T04:55:13.6387628Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T04:55:13.6387967Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T04:55:13.6388315Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T04:55:13.6388574Z         
2025-03-14T04:55:13.6389018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:13.6389562Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T04:55:13.6389878Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T04:55:13.6390205Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T04:55:13.6390553Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T04:55:13.6390808Z         
2025-03-14T04:55:13.6391229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:13.6391738Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T04:55:13.6392088Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T04:55:13.6392447Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T04:55:13.6392711Z         
2025-03-14T04:55:13.6393140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:13.6393653Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T04:55:13.6393998Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T04:55:13.6394363Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T04:55:13.6394622Z         
2025-03-14T04:55:13.6395028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:13.6395504Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T04:55:13.6395775Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T04:55:13.6396021Z         
2025-03-14T04:55:13.6396425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:13.6396897Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T04:55:13.6397184Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T04:55:13.6397421Z         
2025-03-14T04:55:13.6397819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:13.6398320Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T04:55:13.6398643Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T04:55:13.6398897Z         
2025-03-14T04:55:13.6399297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:13.6399793Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T04:55:13.6400099Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T04:55:13.6400358Z         
2025-03-14T04:55:13.6400809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:13.6401432Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T04:55:13.6401740Z         
2025-03-14T04:55:13.6402162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:13.6402714Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T04:55:13.6402995Z         
2025-03-14T04:55:13.6403468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:13.6404081Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T04:55:13.6404369Z         
2025-03-14T04:55:13.6404957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:55:13.6405681Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:55:13.6405939Z         
2025-03-14T04:55:13.6406327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6406830Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T04:55:13.6407093Z         
2025-03-14T04:55:13.6407638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:13.6408266Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T04:55:13.6408540Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T04:55:13.6408826Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:55:13.6409056Z         
2025-03-14T04:55:13.6409610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:13.6410275Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:55:13.6410731Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T04:55:13.6411095Z         
2025-03-14T04:55:13.6411762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:13.6412566Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:13.6412857Z         
2025-03-14T04:55:13.6413261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6413743Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T04:55:13.6413993Z         
2025-03-14T04:55:13.6414532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:13.6415156Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T04:55:13.6415442Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T04:55:13.6415732Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T04:55:13.6415975Z         
2025-03-14T04:55:13.6416533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:13.6417199Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:55:13.6417633Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T04:55:13.6417992Z         
2025-03-14T04:55:13.6418540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:13.6419232Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:13.6419537Z         
2025-03-14T04:55:13.6419916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6420408Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T04:55:13.6420653Z         
2025-03-14T04:55:13.6421188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:13.6421810Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T04:55:13.6422090Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T04:55:13.6422372Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T04:55:13.6422612Z         
2025-03-14T04:55:13.6423165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:13.6423826Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:55:13.6424262Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T04:55:13.6424623Z         
2025-03-14T04:55:13.6425193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:13.6425875Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:13.6426184Z         
2025-03-14T04:55:13.6426567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6427721Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T04:55:13.6427988Z         
2025-03-14T04:55:13.6428525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:13.6429141Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T04:55:13.6429426Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T04:55:13.6429699Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T04:55:13.6429929Z         
2025-03-14T04:55:13.6430464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:13.6431100Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:55:13.6431525Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T04:55:13.6431867Z         
2025-03-14T04:55:13.6432400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:13.6433062Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:13.6433341Z         
2025-03-14T04:55:13.6433711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6434201Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T04:55:13.6434440Z         
2025-03-14T04:55:13.6434949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:13.6435535Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T04:55:13.6435805Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T04:55:13.6436082Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T04:55:13.6436314Z         
2025-03-14T04:55:13.6436843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:13.6437511Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:55:13.6437966Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T04:55:13.6438317Z         
2025-03-14T04:55:13.6438861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:13.6439554Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:13.6439837Z         
2025-03-14T04:55:13.6440219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6440717Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T04:55:13.6440964Z         
2025-03-14T04:55:13.6441351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:13.6442084Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T04:55:13.6442576Z         
2025-03-14T04:55:13.6442945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:13.6443751Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T04:55:13.6444341Z         
2025-03-14T04:55:13.6444708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:13.6445246Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T04:55:13.6445557Z         
2025-03-14T04:55:13.6446033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:55:13.6446615Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T04:55:13.6446882Z         
2025-03-14T04:55:13.6447268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:55:13.6447791Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T04:55:13.6448060Z         
2025-03-14T04:55:13.6448529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:55:13.6449102Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T04:55:13.6449354Z         
2025-03-14T04:55:13.6449935Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:55:13.6450615Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T04:55:13.6450931Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:55:13.6451277Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:55:13.6451723Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:55:13.6452003Z         
2025-03-14T04:55:13.6452507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:55:13.6453060Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:55:13.6453318Z         
2025-03-14T04:55:13.6453408Z 
2025-03-14T04:55:13.6453506Z class GraphModule(torch.nn.Module):
2025-03-14T04:55:13.6455815Z     def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T04:55:13.6458213Z         l_features_p2_ = L_features_p2_
2025-03-14T04:55:13.6458436Z         l_features_p3_ = L_features_p3_
2025-03-14T04:55:13.6458652Z         l_features_p4_ = L_features_p4_
2025-03-14T04:55:13.6458866Z         l_features_p5_ = L_features_p5_
2025-03-14T04:55:13.6459078Z         l_features_p6_ = L_features_p6_
2025-03-14T04:55:13.6459470Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:55:13.6460042Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T04:55:13.6460796Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T04:55:13.6461385Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T04:55:13.6462003Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T04:55:13.6462542Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:55:13.6463040Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:55:13.6463594Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:55:13.6464207Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:55:13.6464792Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:55:13.6465357Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:55:13.6465729Z         
2025-03-14T04:55:13.6466296Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6466979Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T04:55:13.6467261Z         
2025-03-14T04:55:13.6467686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6468197Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:55:13.6468494Z         
2025-03-14T04:55:13.6469069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6469742Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T04:55:13.6470020Z         
2025-03-14T04:55:13.6470415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6470921Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:55:13.6471191Z         
2025-03-14T04:55:13.6471669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:55:13.6472305Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:55:13.6472648Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T04:55:13.6472931Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:55:13.6473177Z         
2025-03-14T04:55:13.6473605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:55:13.6474139Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:55:13.6474386Z         
2025-03-14T04:55:13.6474815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:55:13.6475334Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:55:13.6475584Z         
2025-03-14T04:55:13.6476070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:55:13.6476776Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:55:13.6477120Z         
2025-03-14T04:55:13.6477644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:55:13.6478267Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:55:13.6478786Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:55:13.6479297Z         add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:55:13.6479607Z         x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:55:13.6479847Z         
2025-03-14T04:55:13.6480393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6481067Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T04:55:13.6481330Z         
2025-03-14T04:55:13.6481757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6482249Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:55:13.6482535Z         
2025-03-14T04:55:13.6483106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6483775Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T04:55:13.6484060Z         
2025-03-14T04:55:13.6484461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6484970Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T04:55:13.6485245Z         
2025-03-14T04:55:13.6485723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:55:13.6486379Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T04:55:13.6486754Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T04:55:13.6487051Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T04:55:13.6487306Z         
2025-03-14T04:55:13.6487741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:55:13.6488311Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T04:55:13.6488584Z         
2025-03-14T04:55:13.6489042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:55:13.6489609Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T04:55:13.6489881Z         
2025-03-14T04:55:13.6490401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:55:13.6491148Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T04:55:13.6491578Z         
2025-03-14T04:55:13.6492135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:55:13.6492797Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T04:55:13.6493341Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T04:55:13.6493855Z         add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T04:55:13.6494172Z         x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4);  add_1 = None
2025-03-14T04:55:13.6494417Z         
2025-03-14T04:55:13.6494962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6495619Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T04:55:13.6495899Z         
2025-03-14T04:55:13.6496318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6496831Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T04:55:13.6497116Z         
2025-03-14T04:55:13.6497720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6498380Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T04:55:13.6498659Z         
2025-03-14T04:55:13.6499050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6499557Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T04:55:13.6499825Z         
2025-03-14T04:55:13.6500298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:55:13.6500941Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T04:55:13.6501304Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T04:55:13.6501590Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T04:55:13.6501837Z         
2025-03-14T04:55:13.6502266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:55:13.6502799Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T04:55:13.6503053Z         
2025-03-14T04:55:13.6503479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:55:13.6504001Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T04:55:13.6504254Z         
2025-03-14T04:55:13.6504743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:55:13.6505442Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T04:55:13.6505784Z         
2025-03-14T04:55:13.6506309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:55:13.6506935Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T04:55:13.6507456Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T04:55:13.6507972Z         add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T04:55:13.6508284Z         x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4);  add_2 = None
2025-03-14T04:55:13.6508533Z         
2025-03-14T04:55:13.6509073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6509734Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T04:55:13.6510007Z         
2025-03-14T04:55:13.6510424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6510934Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T04:55:13.6511204Z         
2025-03-14T04:55:13.6511754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6512419Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T04:55:13.6512685Z         
2025-03-14T04:55:13.6513077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6513587Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T04:55:13.6513857Z         
2025-03-14T04:55:13.6514231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:55:13.6514422Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T04:55:13.6514529Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T04:55:13.6514647Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T04:55:13.6514719Z         
2025-03-14T04:55:13.6515040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:55:13.6515167Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T04:55:13.6515226Z         
2025-03-14T04:55:13.6515554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:55:13.6515675Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T04:55:13.6515736Z         
2025-03-14T04:55:13.6516118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:55:13.6516352Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T04:55:13.6516418Z         
2025-03-14T04:55:13.6516822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:55:13.6516951Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T04:55:13.6517252Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T04:55:13.6534733Z         add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T04:55:13.6535051Z         x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T04:55:13.6535134Z         
2025-03-14T04:55:13.6535643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6535824Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:55:13.6535891Z         
2025-03-14T04:55:13.6536320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6536472Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T04:55:13.6536590Z         
2025-03-14T04:55:13.6537057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:13.6537218Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:55:13.6537282Z         
2025-03-14T04:55:13.6537586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6537727Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T04:55:13.6537802Z         
2025-03-14T04:55:13.6538177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:55:13.6538386Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T04:55:13.6538488Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T04:55:13.6538621Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T04:55:13.6538683Z         
2025-03-14T04:55:13.6539023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:55:13.6539150Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T04:55:13.6539224Z         
2025-03-14T04:55:13.6539551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:55:13.6539678Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T04:55:13.6539739Z         
2025-03-14T04:55:13.6540131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:55:13.6540380Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T04:55:13.6540443Z         
2025-03-14T04:55:13.6540870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:55:13.6540997Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T04:55:13.6541320Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T04:55:13.6541443Z         add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T04:55:13.6541565Z         x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T04:55:13.6541629Z         
2025-03-14T04:55:13.6541937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:55:13.6542059Z         tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:55:13.6542190Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32);  x_1 = None
2025-03-14T04:55:13.6542336Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32);  x_2 = None
2025-03-14T04:55:13.6542465Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32);  x_3 = None
2025-03-14T04:55:13.6542580Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32);  x_4 = None
2025-03-14T04:55:13.6542669Z         
2025-03-14T04:55:13.6542950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:13.6543402Z         x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p2_ = None
2025-03-14T04:55:13.6543466Z         
2025-03-14T04:55:13.6543750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:55:13.6543938Z         x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False);  x_5 = None
2025-03-14T04:55:13.6544007Z         
2025-03-14T04:55:13.6544383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:55:13.6544808Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:55:13.6544871Z         
2025-03-14T04:55:13.6545235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:55:13.6545651Z         x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_6 = None
2025-03-14T04:55:13.6545713Z         
2025-03-14T04:55:13.6545976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:13.6546401Z         x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p3_ = None
2025-03-14T04:55:13.6546471Z         
2025-03-14T04:55:13.6546744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:55:13.6546933Z         x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False);  x_7 = None
2025-03-14T04:55:13.6546995Z         
2025-03-14T04:55:13.6547374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:55:13.6547778Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:55:13.6547848Z         
2025-03-14T04:55:13.6548200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:55:13.6548622Z         x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_8 = None
2025-03-14T04:55:13.6548685Z         
2025-03-14T04:55:13.6548956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:13.6549428Z         x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p4_ = None
2025-03-14T04:55:13.6549502Z         
2025-03-14T04:55:13.6549793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:55:13.6549973Z         x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False);  x_9 = None
2025-03-14T04:55:13.6550043Z         
2025-03-14T04:55:13.6550426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:55:13.6550824Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:55:13.6550889Z         
2025-03-14T04:55:13.6551249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:55:13.6551639Z         x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_10 = None
2025-03-14T04:55:13.6551710Z         
2025-03-14T04:55:13.6551962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:13.6552362Z         x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p5_ = None
2025-03-14T04:55:13.6552442Z         
2025-03-14T04:55:13.6552723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:55:13.6552898Z         x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False);  x_11 = None
2025-03-14T04:55:13.6552971Z         
2025-03-14T04:55:13.6553344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:55:13.6553739Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:55:13.6553810Z         
2025-03-14T04:55:13.6554170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:55:13.6554567Z         x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_12 = None
2025-03-14T04:55:13.6554628Z         
2025-03-14T04:55:13.6554884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:13.6555480Z         x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:55:13.6555571Z         
2025-03-14T04:55:13.6555860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:55:13.6556040Z         x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False);  x_13 = None
2025-03-14T04:55:13.6556106Z         
2025-03-14T04:55:13.6556482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:55:13.6557130Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:55:13.6557202Z         
2025-03-14T04:55:13.6557560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:55:13.6558165Z         x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:55:13.6558228Z         
2025-03-14T04:55:13.6558574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:55:13.6558742Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:55:13.6558905Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:55:13.6559067Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T04:55:13.6559217Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T04:55:13.6559365Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T04:55:13.6559505Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T04:55:13.6559650Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T04:55:13.6559791Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T04:55:13.6559935Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T04:55:13.6560074Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T04:55:13.6560135Z         
2025-03-14T04:55:13.6560760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:55:13.6560944Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304);  x_15 = None
2025-03-14T04:55:13.6561197Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T04:55:13.6561389Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T04:55:13.6561587Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152);  x_16 = None
2025-03-14T04:55:13.6561803Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T04:55:13.6561980Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T04:55:13.6562141Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76);  x_17 = None
2025-03-14T04:55:13.6562305Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T04:55:13.6562480Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T04:55:13.6562619Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38);  x_18 = None
2025-03-14T04:55:13.6562783Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T04:55:13.6562946Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T04:55:13.6563088Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19);  x_19 = None
2025-03-14T04:55:13.6563241Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T04:55:13.6563410Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T04:55:13.6563473Z         
2025-03-14T04:55:13.6563887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:13.6564087Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:55:13.6564156Z         
2025-03-14T04:55:13.6564623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:13.6564783Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:55:13.6564929Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:55:13.6565073Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:55:13.6565137Z         
2025-03-14T04:55:13.6565524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:13.6565699Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:55:13.6565761Z         
2025-03-14T04:55:13.6566078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:13.6566218Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:55:13.6566288Z         
2025-03-14T04:55:13.6566600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:13.6566752Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:55:13.6566884Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6567045Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:55:13.6568096Z         
2025-03-14T04:55:13.6568446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:13.6568576Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:55:13.6568705Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:55:13.6568859Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T04:55:13.6568931Z         
2025-03-14T04:55:13.6569246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:13.6569378Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6569468Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T04:55:13.6569604Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T04:55:13.6569669Z         
2025-03-14T04:55:13.6569992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:13.6570140Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:55:13.6570239Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T04:55:13.6570371Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T04:55:13.6570443Z         
2025-03-14T04:55:13.6570769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:13.6570936Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:13.6571055Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T04:55:13.6571144Z         
2025-03-14T04:55:13.6571570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:13.6571757Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:13.6571875Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T04:55:13.6571949Z         
2025-03-14T04:55:13.6572262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:13.6572435Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:13.6572546Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T04:55:13.6572618Z         
2025-03-14T04:55:13.6572923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:13.6573122Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:55:13.6573234Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T04:55:13.6573307Z         
2025-03-14T04:55:13.6573647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:13.6573818Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:55:13.6573887Z         
2025-03-14T04:55:13.6574227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:13.6574389Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:55:13.6574452Z         
2025-03-14T04:55:13.6574828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:13.6574970Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:55:13.6575105Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T04:55:13.6575264Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:55:13.6575411Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T04:55:13.6575474Z         
2025-03-14T04:55:13.6575840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:13.6575984Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:55:13.6576116Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T04:55:13.6576267Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:55:13.6576412Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T04:55:13.6576474Z         
2025-03-14T04:55:13.6576818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:13.6576937Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:55:13.6577108Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:55:13.6577261Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T04:55:13.6577333Z         
2025-03-14T04:55:13.6577669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:13.6577793Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:55:13.6577960Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:55:13.6578107Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T04:55:13.6578168Z         
2025-03-14T04:55:13.6578492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:13.6578591Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:55:13.6578717Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:55:13.6578778Z         
2025-03-14T04:55:13.6579098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:13.6579188Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:55:13.6579313Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:55:13.6579375Z         
2025-03-14T04:55:13.6579709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:13.6579824Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:55:13.6579982Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:55:13.6580044Z         
2025-03-14T04:55:13.6580372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:13.6580487Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:55:13.6580622Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:55:13.6580683Z         
2025-03-14T04:55:13.6581041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:13.6581224Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:55:13.6581297Z         
2025-03-14T04:55:13.6581630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:13.6581807Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T04:55:13.6581871Z         
2025-03-14T04:55:13.6582267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:13.6582455Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:55:13.6582518Z         
2025-03-14T04:55:13.6582940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:13.6583146Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T04:55:13.6583216Z         
2025-03-14T04:55:13.6583666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:13.6583826Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T04:55:13.6583974Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T04:55:13.6584119Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T04:55:13.6584182Z         
2025-03-14T04:55:13.6584561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:13.6584731Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T04:55:13.6584799Z         
2025-03-14T04:55:13.6585110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:13.6585264Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T04:55:13.6585326Z         
2025-03-14T04:55:13.6585642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:13.6585788Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T04:55:13.6585923Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6586068Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T04:55:13.6586164Z         
2025-03-14T04:55:13.6586477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:13.6586625Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T04:55:13.6586747Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T04:55:13.6586905Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T04:55:13.6586968Z         
2025-03-14T04:55:13.6587281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:13.6587402Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6587499Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T04:55:13.6587631Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T04:55:13.6587702Z         
2025-03-14T04:55:13.6588006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:13.6588160Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T04:55:13.6588252Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T04:55:13.6588390Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T04:55:13.6588451Z         
2025-03-14T04:55:13.6588758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:13.6588910Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:13.6589037Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T04:55:13.6589122Z         
2025-03-14T04:55:13.6589416Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:13.6589571Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:13.6589682Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T04:55:13.6589748Z         
2025-03-14T04:55:13.6590040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:13.6590193Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:13.6590302Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T04:55:13.6590374Z         
2025-03-14T04:55:13.6590676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:13.6590866Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T04:55:13.6590973Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T04:55:13.6591043Z         
2025-03-14T04:55:13.6591373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:13.6591534Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T04:55:13.6591596Z         
2025-03-14T04:55:13.6591932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:13.6592092Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T04:55:13.6592167Z         
2025-03-14T04:55:13.6592516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:13.6592651Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T04:55:13.6592782Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T04:55:13.6592938Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T04:55:13.6593083Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T04:55:13.6593146Z         
2025-03-14T04:55:13.6593494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:13.6593632Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T04:55:13.6593760Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T04:55:13.6593911Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T04:55:13.6594053Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T04:55:13.6594114Z         
2025-03-14T04:55:13.6594447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:13.6594561Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T04:55:13.6594729Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T04:55:13.6594899Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T04:55:13.6594967Z         
2025-03-14T04:55:13.6595295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:13.6595409Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T04:55:13.6595577Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T04:55:13.6595715Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T04:55:13.6595775Z         
2025-03-14T04:55:13.6596085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:13.6596182Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T04:55:13.6596305Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T04:55:13.6596364Z         
2025-03-14T04:55:13.6596672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:13.6596763Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T04:55:13.6596885Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T04:55:13.6596945Z         
2025-03-14T04:55:13.6597267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:13.6597394Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T04:55:13.6597530Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T04:55:13.6597592Z         
2025-03-14T04:55:13.6597913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:13.6598027Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T04:55:13.6598162Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T04:55:13.6598222Z         
2025-03-14T04:55:13.6598571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:13.6598766Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T04:55:13.6598837Z         
2025-03-14T04:55:13.6599171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:13.6599341Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T04:55:13.6599401Z         
2025-03-14T04:55:13.6599790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:13.6599972Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T04:55:13.6600034Z         
2025-03-14T04:55:13.6600442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:13.6600648Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T04:55:13.6600732Z         
2025-03-14T04:55:13.6601165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:13.6601322Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T04:55:13.6601470Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T04:55:13.6601614Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T04:55:13.6601674Z         
2025-03-14T04:55:13.6602052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:13.6602219Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T04:55:13.6602288Z         
2025-03-14T04:55:13.6602595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:13.6602741Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T04:55:13.6602799Z         
2025-03-14T04:55:13.6603126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:13.6603253Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T04:55:13.6603380Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6603542Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T04:55:13.6603611Z         
2025-03-14T04:55:13.6603943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:13.6604071Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T04:55:13.6604187Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T04:55:13.6604341Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T04:55:13.6604400Z         
2025-03-14T04:55:13.6604717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:13.6604837Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6604929Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T04:55:13.6605058Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T04:55:13.6605126Z         
2025-03-14T04:55:13.6605439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:13.6605588Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T04:55:13.6605677Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T04:55:13.6605813Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T04:55:13.6605877Z         
2025-03-14T04:55:13.6606196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:13.6606351Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:13.6606486Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T04:55:13.6606551Z         
2025-03-14T04:55:13.6606858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:13.6607014Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:13.6607125Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T04:55:13.6607192Z         
2025-03-14T04:55:13.6607494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:13.6607649Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:13.6607759Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T04:55:13.6607831Z         
2025-03-14T04:55:13.6608135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:13.6608324Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T04:55:13.6608433Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T04:55:13.6608499Z         
2025-03-14T04:55:13.6608863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:13.6609010Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T04:55:13.6609089Z         
2025-03-14T04:55:13.6609433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:13.6609586Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T04:55:13.6609657Z         
2025-03-14T04:55:13.6610004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:13.6610143Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T04:55:13.6610269Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T04:55:13.6610427Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T04:55:13.6610563Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T04:55:13.6610634Z         
2025-03-14T04:55:13.6610981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:13.6611125Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T04:55:13.6611248Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T04:55:13.6611495Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T04:55:13.6611677Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T04:55:13.6611757Z         
2025-03-14T04:55:13.6612121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:13.6612255Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T04:55:13.6612457Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T04:55:13.6612609Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T04:55:13.6612676Z         
2025-03-14T04:55:13.6613041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:13.6613163Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T04:55:13.6613332Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T04:55:13.6613471Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T04:55:13.6613535Z         
2025-03-14T04:55:13.6613853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:13.6613952Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T04:55:13.6614074Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T04:55:13.6614135Z         
2025-03-14T04:55:13.6614453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:13.6614548Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T04:55:13.6614685Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T04:55:13.6614748Z         
2025-03-14T04:55:13.6615060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:13.6615194Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T04:55:13.6615333Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T04:55:13.6615411Z         
2025-03-14T04:55:13.6615728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:13.6615842Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T04:55:13.6615981Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T04:55:13.6616043Z         
2025-03-14T04:55:13.6616413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:13.6616605Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T04:55:13.6616675Z         
2025-03-14T04:55:13.6617018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:13.6617188Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T04:55:13.6617252Z         
2025-03-14T04:55:13.6617654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:13.6617829Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T04:55:13.6617899Z         
2025-03-14T04:55:13.6618312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:13.6618545Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T04:55:13.6618608Z         
2025-03-14T04:55:13.6619053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:13.6619204Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T04:55:13.6619362Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T04:55:13.6619498Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T04:55:13.6619566Z         
2025-03-14T04:55:13.6619940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:13.6620116Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T04:55:13.6620177Z         
2025-03-14T04:55:13.6620498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:13.6620647Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T04:55:13.6620709Z         
2025-03-14T04:55:13.6621048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:13.6621178Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T04:55:13.6621359Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6621508Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T04:55:13.6621581Z         
2025-03-14T04:55:13.6621921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:13.6622056Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T04:55:13.6622176Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T04:55:13.6622335Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T04:55:13.6622397Z         
2025-03-14T04:55:13.6622715Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:13.6622836Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6622933Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T04:55:13.6623063Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T04:55:13.6623129Z         
2025-03-14T04:55:13.6623452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:13.6623603Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T04:55:13.6623691Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T04:55:13.6623822Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T04:55:13.6623882Z         
2025-03-14T04:55:13.6624188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:13.6624339Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:13.6624470Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T04:55:13.6624530Z         
2025-03-14T04:55:13.6624826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:13.6624974Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:13.6625088Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T04:55:13.6625149Z         
2025-03-14T04:55:13.6625446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:13.6625591Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:13.6625707Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T04:55:13.6625765Z         
2025-03-14T04:55:13.6626071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:13.6626248Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T04:55:13.6626361Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T04:55:13.6626420Z         
2025-03-14T04:55:13.6626772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:13.6626916Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T04:55:13.6627000Z         
2025-03-14T04:55:13.6627349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:13.6627486Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T04:55:13.6627554Z         
2025-03-14T04:55:13.6627897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:13.6628040Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T04:55:13.6628165Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T04:55:13.6628322Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T04:55:13.6628462Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T04:55:13.6628533Z         
2025-03-14T04:55:13.6628881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:13.6629025Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T04:55:13.6629146Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T04:55:13.6629305Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T04:55:13.6629442Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T04:55:13.6629514Z         
2025-03-14T04:55:13.6629842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:13.6629963Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T04:55:13.6630143Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T04:55:13.6630278Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T04:55:13.6630338Z         
2025-03-14T04:55:13.6630674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:13.6630782Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T04:55:13.6630950Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T04:55:13.6631075Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T04:55:13.6631145Z         
2025-03-14T04:55:13.6631453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:13.6631555Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T04:55:13.6631667Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T04:55:13.6631734Z         
2025-03-14T04:55:13.6632034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:13.6632148Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T04:55:13.6632261Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T04:55:13.6632328Z         
2025-03-14T04:55:13.6632625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:13.6632761Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T04:55:13.6632903Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T04:55:13.6632972Z         
2025-03-14T04:55:13.6633277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:13.6633392Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T04:55:13.6633517Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T04:55:13.6633584Z         
2025-03-14T04:55:13.6633931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:13.6634122Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T04:55:13.6634183Z         
2025-03-14T04:55:13.6634514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:13.6634677Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T04:55:13.6634736Z         
2025-03-14T04:55:13.6635124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:13.6635295Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T04:55:13.6635364Z         
2025-03-14T04:55:13.6635770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:13.6636009Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T04:55:13.6636069Z         
2025-03-14T04:55:13.6636501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:13.6636645Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T04:55:13.6636799Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T04:55:13.6636930Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T04:55:13.6636999Z         
2025-03-14T04:55:13.6637366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:13.6637536Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T04:55:13.6637597Z         
2025-03-14T04:55:13.6637909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:13.6638045Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T04:55:13.6638126Z         
2025-03-14T04:55:13.6638434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:13.6638583Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T04:55:13.6638701Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6638866Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T04:55:13.6638928Z         
2025-03-14T04:55:13.6639249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:13.6639365Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T04:55:13.6639487Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T04:55:13.6639631Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T04:55:13.6639699Z         
2025-03-14T04:55:13.6640005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:13.6640131Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:55:13.6640217Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T04:55:13.6640349Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T04:55:13.6640409Z         
2025-03-14T04:55:13.6640723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:13.6640863Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T04:55:13.6640957Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T04:55:13.6641081Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T04:55:13.6641148Z         
2025-03-14T04:55:13.6641448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:13.6641619Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:13.6641734Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T04:55:13.6641794Z         
2025-03-14T04:55:13.6642099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:13.6642246Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:13.6642360Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T04:55:13.6642421Z         
2025-03-14T04:55:13.6642722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:13.6642864Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:13.6642980Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T04:55:13.6643041Z         
2025-03-14T04:55:13.6643345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:13.6643521Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T04:55:13.6643631Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T04:55:13.6643707Z         
2025-03-14T04:55:13.6644046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:13.6644195Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T04:55:13.6644261Z         
2025-03-14T04:55:13.6644604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:13.6644742Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T04:55:13.6644801Z         
2025-03-14T04:55:13.6645144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:13.6645275Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T04:55:13.6645399Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T04:55:13.6645545Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T04:55:13.6645684Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T04:55:13.6645746Z         
2025-03-14T04:55:13.6646096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:13.6646227Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T04:55:13.6646361Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T04:55:13.6646507Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T04:55:13.6646644Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T04:55:13.6646704Z         
2025-03-14T04:55:13.6647046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:13.6647187Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T04:55:13.6647349Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T04:55:13.6647487Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T04:55:13.6647551Z         
2025-03-14T04:55:13.6647900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:13.6648012Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T04:55:13.6648184Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T04:55:13.6648315Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T04:55:13.6648384Z         
2025-03-14T04:55:13.6648699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:13.6648800Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T04:55:13.6648914Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T04:55:13.6648983Z         
2025-03-14T04:55:13.6649292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:13.6649410Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T04:55:13.6649525Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T04:55:13.6649593Z         
2025-03-14T04:55:13.6649899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:13.6650042Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T04:55:13.6650197Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T04:55:13.6650269Z         
2025-03-14T04:55:13.6650572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:13.6650690Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T04:55:13.6650821Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T04:55:13.6650891Z         
2025-03-14T04:55:13.6651246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:13.6651534Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T04:55:13.6651609Z         
2025-03-14T04:55:13.6651966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:13.6652131Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T04:55:13.6652203Z         
2025-03-14T04:55:13.6652601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:13.6652783Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T04:55:13.6652850Z         
2025-03-14T04:55:13.6653357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:55:13.6653530Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:55:13.6653601Z         
2025-03-14T04:55:13.6653901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6654053Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T04:55:13.6654117Z         
2025-03-14T04:55:13.6654570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:13.6654687Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T04:55:13.6654796Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T04:55:13.6654909Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:55:13.6654981Z         
2025-03-14T04:55:13.6655449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:13.6655589Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:55:13.6655843Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T04:55:13.6655910Z         
2025-03-14T04:55:13.6656396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:13.6656595Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:13.6656665Z         
2025-03-14T04:55:13.6656964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6657093Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T04:55:13.6657154Z         
2025-03-14T04:55:13.6657598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:13.6657714Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T04:55:13.6657829Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T04:55:13.6657947Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T04:55:13.6658019Z         
2025-03-14T04:55:13.6658487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:13.6658628Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:55:13.6658861Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T04:55:13.6658933Z         
2025-03-14T04:55:13.6659391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:13.6659566Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:13.6659647Z         
2025-03-14T04:55:13.6659955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6660082Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T04:55:13.6660151Z         
2025-03-14T04:55:13.6660793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:13.6660926Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T04:55:13.6661031Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T04:55:13.6661160Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T04:55:13.6661224Z         
2025-03-14T04:55:13.6661708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:13.6661840Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:55:13.6662086Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T04:55:13.6662157Z         
2025-03-14T04:55:13.6662666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:13.6662873Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:13.6662934Z         
2025-03-14T04:55:13.6663258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6663387Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T04:55:13.6663462Z         
2025-03-14T04:55:13.6663900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:13.6664022Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T04:55:13.6664129Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T04:55:13.6664253Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T04:55:13.6664318Z         
2025-03-14T04:55:13.6664791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:13.6664924Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:55:13.6665166Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T04:55:13.6665229Z         
2025-03-14T04:55:13.6665697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:13.6665859Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:13.6665932Z         
2025-03-14T04:55:13.6666229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6666385Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T04:55:13.6666447Z         
2025-03-14T04:55:13.6666892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:13.6667001Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T04:55:13.6667112Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T04:55:13.6667227Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T04:55:13.6667297Z         
2025-03-14T04:55:13.6667760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:13.6667933Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:55:13.6668171Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T04:55:13.6668231Z         
2025-03-14T04:55:13.6668696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:13.6668853Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:13.6668934Z         
2025-03-14T04:55:13.6669225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:13.6669369Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T04:55:13.6669431Z         
2025-03-14T04:55:13.6669713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:13.6670091Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T04:55:13.6670160Z         
2025-03-14T04:55:13.6670435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:13.6670901Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T04:55:13.6670965Z         
2025-03-14T04:55:13.6671244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:13.6671441Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T04:55:13.6671508Z         
2025-03-14T04:55:13.6671889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:55:13.6672034Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T04:55:13.6672094Z         
2025-03-14T04:55:13.6672392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:55:13.6672559Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T04:55:13.6672629Z         
2025-03-14T04:55:13.6673009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:55:13.6673156Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T04:55:13.6673219Z         
2025-03-14T04:55:13.6673706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:55:13.6673847Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T04:55:13.6673967Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:55:13.6674129Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:55:13.6674259Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:55:13.6674329Z         
2025-03-14T04:55:13.6674702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:55:13.6674840Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:55:13.6674903Z         
2025-03-14T04:55:18.9614260Z 
2025-03-14T04:55:18.9615088Z class GraphModule(torch.nn.Module):
2025-03-14T04:55:18.9617821Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 269952, 4][1079808, 4, 1]cpu", L_anchors_0_tensor: "f32[269952, 4][4, 1]cpu", L_pred_anchor_deltas_1_: "f32[4, 67488, 4][269952, 4, 1]cpu", L_anchors_1_tensor: "f32[67488, 4][4, 1]cpu", L_pred_anchor_deltas_2_: "f32[4, 16872, 4][67488, 4, 1]cpu", L_anchors_2_tensor: "f32[16872, 4][4, 1]cpu", L_pred_anchor_deltas_3_: "f32[4, 4218, 4][16872, 4, 1]cpu", L_anchors_3_tensor: "f32[4218, 4][4, 1]cpu", L_pred_anchor_deltas_4_: "f32[4, 1083, 4][4332, 4, 1]cpu", L_anchors_4_tensor: "f32[1083, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 269952][269952, 1]cpu", L_pred_objectness_logits_1_: "f32[4, 67488][67488, 1]cpu", L_pred_objectness_logits_2_: "f32[4, 16872][16872, 1]cpu", L_pred_objectness_logits_3_: "f32[4, 4218][4218, 1]cpu", L_pred_objectness_logits_4_: "f32[4, 1083][1083, 1]cpu"):
2025-03-14T04:55:18.9619354Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T04:55:18.9619615Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T04:55:18.9619860Z         l_pred_anchor_deltas_1_ = L_pred_anchor_deltas_1_
2025-03-14T04:55:18.9620110Z         l_anchors_1_tensor = L_anchors_1_tensor
2025-03-14T04:55:18.9620356Z         l_pred_anchor_deltas_2_ = L_pred_anchor_deltas_2_
2025-03-14T04:55:18.9620599Z         l_anchors_2_tensor = L_anchors_2_tensor
2025-03-14T04:55:18.9620840Z         l_pred_anchor_deltas_3_ = L_pred_anchor_deltas_3_
2025-03-14T04:55:18.9621080Z         l_anchors_3_tensor = L_anchors_3_tensor
2025-03-14T04:55:18.9621317Z         l_pred_anchor_deltas_4_ = L_pred_anchor_deltas_4_
2025-03-14T04:55:18.9621549Z         l_anchors_4_tensor = L_anchors_4_tensor
2025-03-14T04:55:18.9621807Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T04:55:18.9622095Z         l_pred_objectness_logits_1_ = L_pred_objectness_logits_1_
2025-03-14T04:55:18.9622378Z         l_pred_objectness_logits_2_ = L_pred_objectness_logits_2_
2025-03-14T04:55:18.9622658Z         l_pred_objectness_logits_3_ = L_pred_objectness_logits_3_
2025-03-14T04:55:18.9622933Z         l_pred_objectness_logits_4_ = L_pred_objectness_logits_4_
2025-03-14T04:55:18.9623168Z         
2025-03-14T04:55:18.9623723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:18.9624492Z         pred_anchor_deltas_i: "f32[1079808, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T04:55:18.9624840Z         
2025-03-14T04:55:18.9625376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:18.9626066Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T04:55:18.9626473Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:55:18.9626833Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:55:18.9627109Z         
2025-03-14T04:55:18.9627598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:18.9628206Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T04:55:18.9628501Z         
2025-03-14T04:55:18.9628911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:18.9629491Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:55:18.9629772Z         
2025-03-14T04:55:18.9630171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:18.9630697Z         getitem: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:55:18.9631035Z         getitem_1: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:18.9631370Z         widths: "f32[1079808][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T04:55:18.9631639Z         
2025-03-14T04:55:18.9632042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:18.9632547Z         getitem_2: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:55:18.9632854Z         getitem_3: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:55:18.9633173Z         heights: "f32[1079808][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:55:18.9633984Z         
2025-03-14T04:55:18.9634506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:18.9636481Z         getitem_4: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:18.9636755Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T04:55:18.9637025Z         ctr_x: "f32[1079808][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T04:55:18.9637645Z         
2025-03-14T04:55:18.9638121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:18.9638643Z         getitem_5: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:55:18.9638932Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T04:55:18.9639283Z         ctr_y: "f32[1079808][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T04:55:18.9639656Z         
2025-03-14T04:55:18.9640640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:18.9641255Z         getitem_6: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:18.9641588Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T04:55:18.9641830Z         
2025-03-14T04:55:18.9642228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:18.9642740Z         getitem_7: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:18.9643066Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T04:55:18.9643298Z         
2025-03-14T04:55:18.9643685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:18.9646087Z         getitem_8: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:18.9646411Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:55:18.9646642Z         
2025-03-14T04:55:18.9647033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:18.9647572Z         getitem_9: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:55:18.9647987Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:55:18.9648223Z         
2025-03-14T04:55:18.9648650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:18.9649230Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:55:18.9649492Z         
2025-03-14T04:55:18.9649938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:18.9650642Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:55:18.9650900Z         
2025-03-14T04:55:18.9651335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:18.9651974Z         getitem_10: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:55:18.9652318Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T04:55:18.9652677Z         getitem_11: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:55:18.9653043Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T04:55:18.9653314Z         
2025-03-14T04:55:18.9653744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:18.9654279Z         getitem_12: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:55:18.9654601Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T04:55:18.9654932Z         getitem_13: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:55:18.9655280Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T04:55:18.9655539Z         
2025-03-14T04:55:18.9655955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:18.9656491Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:55:18.9656823Z         getitem_14: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:55:18.9657169Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T04:55:18.9658648Z         
2025-03-14T04:55:18.9659951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:18.9661294Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:55:18.9661670Z         getitem_15: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:55:18.9662032Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T04:55:18.9662292Z         
2025-03-14T04:55:18.9662696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:18.9663157Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:55:18.9663422Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:55:18.9663657Z         
2025-03-14T04:55:18.9664162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:18.9664627Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:55:18.9664892Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:55:18.9665172Z         
2025-03-14T04:55:18.9665561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:18.9666073Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:55:18.9666375Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:55:18.9666620Z         
2025-03-14T04:55:18.9667007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:18.9667472Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:55:18.9668106Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:55:18.9668377Z         
2025-03-14T04:55:18.9668817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:18.9669412Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:55:18.9669696Z         
2025-03-14T04:55:18.9670100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:18.9672006Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T04:55:18.9672300Z         
2025-03-14T04:55:18.9672771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:18.9673377Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:55:18.9673671Z         
2025-03-14T04:55:18.9674146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:18.9674916Z         pred_anchor_deltas_i_1: "f32[269952, 4][4, 1]cpu" = l_pred_anchor_deltas_1_.reshape(-1, 4);  l_pred_anchor_deltas_1_ = None
2025-03-14T04:55:18.9675253Z         
2025-03-14T04:55:18.9675757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:18.9676415Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = l_anchors_1_tensor.unsqueeze(0);  l_anchors_1_tensor = None
2025-03-14T04:55:18.9676803Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T04:55:18.9677151Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T04:55:18.9677412Z         
2025-03-14T04:55:18.9677868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:18.9678494Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:55:18.9678798Z         
2025-03-14T04:55:18.9679187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:18.9679721Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T04:55:18.9679988Z         
2025-03-14T04:55:18.9680384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:18.9680908Z         getitem_16: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T04:55:18.9681243Z         getitem_17: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:55:18.9681579Z         widths_1: "f32[269952][1]cpu" = getitem_16 - getitem_17;  getitem_16 = getitem_17 = None
2025-03-14T04:55:18.9681844Z         
2025-03-14T04:55:18.9682240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:18.9682771Z         getitem_18: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T04:55:18.9683090Z         getitem_19: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T04:55:18.9683414Z         heights_1: "f32[269952][1]cpu" = getitem_18 - getitem_19;  getitem_18 = getitem_19 = None
2025-03-14T04:55:18.9684553Z         
2025-03-14T04:55:18.9684996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:18.9685512Z         getitem_20: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:55:18.9685791Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T04:55:18.9686073Z         ctr_x_1: "f32[269952][1]cpu" = getitem_20 + mul_10;  getitem_20 = mul_10 = None
2025-03-14T04:55:18.9686334Z         
2025-03-14T04:55:18.9686738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:18.9687264Z         getitem_21: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T04:55:18.9687566Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T04:55:18.9687845Z         ctr_y_1: "f32[269952][1]cpu" = getitem_21 + mul_11;  getitem_21 = mul_11 = None
2025-03-14T04:55:18.9688098Z         
2025-03-14T04:55:18.9688507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:18.9689052Z         getitem_22: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:18.9689384Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_22 / 1.0;  getitem_22 = None
2025-03-14T04:55:18.9689616Z         
2025-03-14T04:55:18.9689997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:18.9690500Z         getitem_23: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:18.9690827Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_23 / 1.0;  getitem_23 = None
2025-03-14T04:55:18.9691063Z         
2025-03-14T04:55:18.9691548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:18.9692073Z         getitem_24: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:18.9692409Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_24 / 1.0;  getitem_24 = None
2025-03-14T04:55:18.9692655Z         
2025-03-14T04:55:18.9693061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:18.9693626Z         getitem_25: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T04:55:18.9693979Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_25 / 1.0;  getitem_25 = None
2025-03-14T04:55:18.9694209Z         
2025-03-14T04:55:18.9694631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:18.9695203Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T04:55:18.9695468Z         
2025-03-14T04:55:18.9695881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:18.9696405Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T04:55:18.9696665Z         
2025-03-14T04:55:18.9697096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:18.9697631Z         getitem_26: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T04:55:18.9697953Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_26;  dx_1 = getitem_26 = None
2025-03-14T04:55:18.9698292Z         getitem_27: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T04:55:18.9698652Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_27;  mul_12 = getitem_27 = None
2025-03-14T04:55:18.9698911Z         
2025-03-14T04:55:18.9699336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:18.9699873Z         getitem_28: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T04:55:18.9700193Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_28;  dy_1 = getitem_28 = None
2025-03-14T04:55:18.9700527Z         getitem_29: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T04:55:18.9700877Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_29;  mul_13 = getitem_29 = None
2025-03-14T04:55:18.9701130Z         
2025-03-14T04:55:18.9701573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:18.9702085Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T04:55:18.9702421Z         getitem_30: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T04:55:18.9702774Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_30;  exp_2 = getitem_30 = None
2025-03-14T04:55:18.9703028Z         
2025-03-14T04:55:18.9703439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:18.9703975Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T04:55:18.9704305Z         getitem_31: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T04:55:18.9704662Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_31;  exp_3 = getitem_31 = None
2025-03-14T04:55:18.9704909Z         
2025-03-14T04:55:18.9705306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:18.9705763Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T04:55:18.9706026Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T04:55:18.9706258Z         
2025-03-14T04:55:18.9706669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:18.9707128Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T04:55:18.9707414Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T04:55:18.9707650Z         
2025-03-14T04:55:18.9708051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:18.9708529Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T04:55:18.9708834Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T04:55:18.9709085Z         
2025-03-14T04:55:18.9709476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:18.9709956Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T04:55:18.9710258Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T04:55:18.9710506Z         
2025-03-14T04:55:18.9710938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:18.9711535Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T04:55:18.9711842Z         
2025-03-14T04:55:18.9712253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:18.9712808Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T04:55:18.9713091Z         
2025-03-14T04:55:18.9713557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:18.9714188Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T04:55:18.9714499Z         
2025-03-14T04:55:18.9714981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:18.9715648Z         pred_anchor_deltas_i_2: "f32[67488, 4][4, 1]cpu" = l_pred_anchor_deltas_2_.reshape(-1, 4);  l_pred_anchor_deltas_2_ = None
2025-03-14T04:55:18.9716072Z         
2025-03-14T04:55:18.9716625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:18.9717301Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = l_anchors_2_tensor.unsqueeze(0);  l_anchors_2_tensor = None
2025-03-14T04:55:18.9717692Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T04:55:18.9718039Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T04:55:18.9718300Z         
2025-03-14T04:55:18.9718757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:18.9719349Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_2.float();  pred_anchor_deltas_i_2 = None
2025-03-14T04:55:18.9719635Z         
2025-03-14T04:55:18.9720049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:18.9720564Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T04:55:18.9720849Z         
2025-03-14T04:55:18.9721241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:18.9721775Z         getitem_32: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T04:55:18.9722081Z         getitem_33: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:55:18.9722422Z         widths_2: "f32[67488][1]cpu" = getitem_32 - getitem_33;  getitem_32 = getitem_33 = None
2025-03-14T04:55:18.9722696Z         
2025-03-14T04:55:18.9723103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:18.9723609Z         getitem_34: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T04:55:18.9723911Z         getitem_35: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T04:55:18.9724242Z         heights_2: "f32[67488][1]cpu" = getitem_34 - getitem_35;  getitem_34 = getitem_35 = None
2025-03-14T04:55:18.9724508Z         
2025-03-14T04:55:18.9724897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:18.9725380Z         getitem_36: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:55:18.9725649Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T04:55:18.9725922Z         ctr_x_2: "f32[67488][1]cpu" = getitem_36 + mul_20;  getitem_36 = mul_20 = None
2025-03-14T04:55:18.9726176Z         
2025-03-14T04:55:18.9726580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:18.9727101Z         getitem_37: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T04:55:18.9727406Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T04:55:18.9727687Z         ctr_y_2: "f32[67488][1]cpu" = getitem_37 + mul_21;  getitem_37 = mul_21 = None
2025-03-14T04:55:18.9727960Z         
2025-03-14T04:55:18.9728351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:18.9728864Z         getitem_38: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:18.9729199Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_38 / 1.0;  getitem_38 = None
2025-03-14T04:55:18.9729436Z         
2025-03-14T04:55:18.9729828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:18.9730338Z         getitem_39: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:18.9730677Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_39 / 1.0;  getitem_39 = None
2025-03-14T04:55:18.9730918Z         
2025-03-14T04:55:18.9731320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:18.9731944Z         getitem_40: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:18.9732286Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_40 / 1.0;  getitem_40 = None
2025-03-14T04:55:18.9732545Z         
2025-03-14T04:55:18.9732993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:18.9733535Z         getitem_41: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T04:55:18.9733886Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_41 / 1.0;  getitem_41 = None
2025-03-14T04:55:18.9734135Z         
2025-03-14T04:55:18.9734553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:18.9735102Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T04:55:18.9735360Z         
2025-03-14T04:55:18.9735770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:18.9736289Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T04:55:18.9736545Z         
2025-03-14T04:55:18.9736966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:18.9737497Z         getitem_42: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T04:55:18.9737806Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_42;  dx_2 = getitem_42 = None
2025-03-14T04:55:18.9738132Z         getitem_43: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T04:55:18.9738470Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_43;  mul_22 = getitem_43 = None
2025-03-14T04:55:18.9738723Z         
2025-03-14T04:55:18.9739137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:18.9739657Z         getitem_44: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T04:55:18.9739969Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_44;  dy_2 = getitem_44 = None
2025-03-14T04:55:18.9740296Z         getitem_45: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T04:55:18.9740640Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_45;  mul_23 = getitem_45 = None
2025-03-14T04:55:18.9740916Z         
2025-03-14T04:55:18.9741327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:18.9741828Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T04:55:18.9742150Z         getitem_46: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T04:55:18.9742501Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_46;  exp_4 = getitem_46 = None
2025-03-14T04:55:18.9742754Z         
2025-03-14T04:55:18.9743162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:18.9743663Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T04:55:18.9743992Z         getitem_47: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T04:55:18.9744346Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_47;  exp_5 = getitem_47 = None
2025-03-14T04:55:18.9744600Z         
2025-03-14T04:55:18.9745000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:18.9745460Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T04:55:18.9745743Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T04:55:18.9745986Z         
2025-03-14T04:55:18.9746378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:18.9746849Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T04:55:18.9747105Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T04:55:18.9747338Z         
2025-03-14T04:55:18.9747743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:18.9748223Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T04:55:18.9748526Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T04:55:18.9748780Z         
2025-03-14T04:55:18.9749169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:18.9749647Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T04:55:18.9749942Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T04:55:18.9750193Z         
2025-03-14T04:55:18.9750626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:18.9751214Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T04:55:18.9751515Z         
2025-03-14T04:55:18.9751926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:18.9752473Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T04:55:18.9752752Z         
2025-03-14T04:55:18.9753213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:18.9753841Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T04:55:18.9754129Z         
2025-03-14T04:55:18.9754609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:18.9755270Z         pred_anchor_deltas_i_3: "f32[16872, 4][4, 1]cpu" = l_pred_anchor_deltas_3_.reshape(-1, 4);  l_pred_anchor_deltas_3_ = None
2025-03-14T04:55:18.9755594Z         
2025-03-14T04:55:18.9756104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:18.9756770Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = l_anchors_3_tensor.unsqueeze(0);  l_anchors_3_tensor = None
2025-03-14T04:55:18.9757152Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T04:55:18.9757493Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T04:55:18.9757750Z         
2025-03-14T04:55:18.9758201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:18.9758788Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T04:55:18.9759095Z         
2025-03-14T04:55:18.9759489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:18.9760017Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T04:55:18.9760282Z         
2025-03-14T04:55:18.9761340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:18.9761979Z         getitem_48: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T04:55:18.9762287Z         getitem_49: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:55:18.9762615Z         widths_3: "f32[16872][1]cpu" = getitem_48 - getitem_49;  getitem_48 = getitem_49 = None
2025-03-14T04:55:18.9762891Z         
2025-03-14T04:55:18.9763306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:18.9763815Z         getitem_50: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T04:55:18.9764131Z         getitem_51: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T04:55:18.9764449Z         heights_3: "f32[16872][1]cpu" = getitem_50 - getitem_51;  getitem_50 = getitem_51 = None
2025-03-14T04:55:18.9764724Z         
2025-03-14T04:55:18.9765129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:18.9765630Z         getitem_52: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:55:18.9765901Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T04:55:18.9766180Z         ctr_x_3: "f32[16872][1]cpu" = getitem_52 + mul_30;  getitem_52 = mul_30 = None
2025-03-14T04:55:18.9766438Z         
2025-03-14T04:55:18.9766845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:18.9767372Z         getitem_53: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T04:55:18.9767676Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T04:55:18.9767994Z         ctr_y_3: "f32[16872][1]cpu" = getitem_53 + mul_31;  getitem_53 = mul_31 = None
2025-03-14T04:55:18.9768253Z         
2025-03-14T04:55:18.9768663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:18.9769179Z         getitem_54: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:18.9769516Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_54 / 1.0;  getitem_54 = None
2025-03-14T04:55:18.9769759Z         
2025-03-14T04:55:18.9770240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:18.9771624Z         getitem_55: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:18.9772020Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_55 / 1.0;  getitem_55 = None
2025-03-14T04:55:18.9772276Z         
2025-03-14T04:55:18.9772691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:18.9773284Z         getitem_56: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:18.9773997Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_56 / 1.0;  getitem_56 = None
2025-03-14T04:55:18.9774244Z         
2025-03-14T04:55:18.9774706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:18.9775264Z         getitem_57: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T04:55:18.9775678Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_57 / 1.0;  getitem_57 = None
2025-03-14T04:55:18.9775919Z         
2025-03-14T04:55:18.9776375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:18.9776920Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T04:55:18.9777189Z         
2025-03-14T04:55:18.9777621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:18.9778163Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T04:55:18.9778426Z         
2025-03-14T04:55:18.9778866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:18.9779426Z         getitem_58: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T04:55:18.9779751Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_58;  dx_3 = getitem_58 = None
2025-03-14T04:55:18.9780088Z         getitem_59: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T04:55:18.9780439Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_59;  mul_32 = getitem_59 = None
2025-03-14T04:55:18.9780694Z         
2025-03-14T04:55:18.9781128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:18.9782214Z         getitem_60: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T04:55:18.9782612Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_60;  dy_3 = getitem_60 = None
2025-03-14T04:55:18.9783039Z         getitem_61: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T04:55:18.9783422Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_61;  mul_33 = getitem_61 = None
2025-03-14T04:55:18.9783936Z         
2025-03-14T04:55:18.9784435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:18.9784953Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T04:55:18.9785293Z         getitem_62: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T04:55:18.9785647Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_62;  exp_6 = getitem_62 = None
2025-03-14T04:55:18.9785904Z         
2025-03-14T04:55:18.9786314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:18.9786832Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T04:55:18.9787160Z         getitem_63: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T04:55:18.9787515Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_63;  exp_7 = getitem_63 = None
2025-03-14T04:55:18.9787768Z         
2025-03-14T04:55:18.9788577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:18.9789082Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T04:55:18.9789354Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T04:55:18.9789598Z         
2025-03-14T04:55:18.9790000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:18.9790484Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T04:55:18.9790766Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T04:55:18.9791001Z         
2025-03-14T04:55:18.9791393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:18.9791867Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T04:55:18.9792168Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T04:55:18.9792427Z         
2025-03-14T04:55:18.9792814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:18.9793363Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T04:55:18.9793739Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T04:55:18.9793990Z         
2025-03-14T04:55:18.9794423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:18.9795011Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T04:55:18.9795312Z         
2025-03-14T04:55:18.9795728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:18.9798680Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T04:55:18.9798983Z         
2025-03-14T04:55:18.9799452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:18.9800113Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T04:55:18.9800402Z         
2025-03-14T04:55:18.9800880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:18.9801537Z         pred_anchor_deltas_i_4: "f32[4332, 4][4, 1]cpu" = l_pred_anchor_deltas_4_.reshape(-1, 4);  l_pred_anchor_deltas_4_ = None
2025-03-14T04:55:18.9801858Z         
2025-03-14T04:55:18.9802376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:18.9803053Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = l_anchors_4_tensor.unsqueeze(0);  l_anchors_4_tensor = None
2025-03-14T04:55:18.9803448Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T04:55:18.9803794Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T04:55:18.9804066Z         
2025-03-14T04:55:18.9804519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:18.9805137Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_4.float();  pred_anchor_deltas_i_4 = None
2025-03-14T04:55:18.9805414Z         
2025-03-14T04:55:18.9805800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:18.9806322Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T04:55:18.9806579Z         
2025-03-14T04:55:18.9806996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:18.9807491Z         getitem_64: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T04:55:18.9807794Z         getitem_65: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:55:18.9808116Z         widths_4: "f32[4332][1]cpu" = getitem_64 - getitem_65;  getitem_64 = getitem_65 = None
2025-03-14T04:55:18.9808381Z         
2025-03-14T04:55:18.9808796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:18.9809490Z         getitem_66: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T04:55:18.9809800Z         getitem_67: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T04:55:18.9810119Z         heights_4: "f32[4332][1]cpu" = getitem_66 - getitem_67;  getitem_66 = getitem_67 = None
2025-03-14T04:55:18.9810382Z         
2025-03-14T04:55:18.9810772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:18.9811260Z         getitem_68: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:55:18.9811626Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T04:55:18.9811912Z         ctr_x_4: "f32[4332][1]cpu" = getitem_68 + mul_40;  getitem_68 = mul_40 = None
2025-03-14T04:55:18.9812505Z         
2025-03-14T04:55:18.9813251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:18.9815440Z         getitem_69: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T04:55:18.9816023Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T04:55:18.9816424Z         ctr_y_4: "f32[4332][1]cpu" = getitem_69 + mul_41;  getitem_69 = mul_41 = None
2025-03-14T04:55:18.9816686Z         
2025-03-14T04:55:18.9817114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:18.9817633Z         getitem_70: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:18.9817964Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_70 / 1.0;  getitem_70 = None
2025-03-14T04:55:18.9818206Z         
2025-03-14T04:55:18.9818605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:18.9819136Z         getitem_71: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:18.9819467Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_71 / 1.0;  getitem_71 = None
2025-03-14T04:55:18.9819702Z         
2025-03-14T04:55:18.9820218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:18.9823685Z         getitem_72: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:18.9824023Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_72 / 1.0;  getitem_72 = None
2025-03-14T04:55:18.9824255Z         
2025-03-14T04:55:18.9824746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:18.9825356Z         getitem_73: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T04:55:18.9825740Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_73 / 1.0;  getitem_73 = None
2025-03-14T04:55:18.9825982Z         
2025-03-14T04:55:18.9826441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:18.9826983Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T04:55:18.9827945Z         
2025-03-14T04:55:18.9828399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:18.9828921Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T04:55:18.9829178Z         
2025-03-14T04:55:18.9829615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:18.9830150Z         getitem_74: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T04:55:18.9830465Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_74;  dx_4 = getitem_74 = None
2025-03-14T04:55:18.9830804Z         getitem_75: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T04:55:18.9831159Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_75;  mul_42 = getitem_75 = None
2025-03-14T04:55:18.9831411Z         
2025-03-14T04:55:18.9831838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:18.9832368Z         getitem_76: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T04:55:18.9832698Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_76;  dy_4 = getitem_76 = None
2025-03-14T04:55:18.9833059Z         getitem_77: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T04:55:18.9833397Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_77;  mul_43 = getitem_77 = None
2025-03-14T04:55:18.9833645Z         
2025-03-14T04:55:18.9834059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:18.9834558Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T04:55:18.9834885Z         getitem_78: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T04:55:18.9835231Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_78;  exp_8 = getitem_78 = None
2025-03-14T04:55:18.9835473Z         
2025-03-14T04:55:18.9835891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:18.9836386Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T04:55:18.9836716Z         getitem_79: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T04:55:18.9837061Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_79;  exp_9 = getitem_79 = None
2025-03-14T04:55:18.9837308Z         
2025-03-14T04:55:18.9837704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:18.9838214Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T04:55:18.9838480Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T04:55:18.9838734Z         
2025-03-14T04:55:18.9839126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:18.9839581Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T04:55:18.9839856Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T04:55:18.9840091Z         
2025-03-14T04:55:18.9840488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:18.9840969Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T04:55:18.9841275Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T04:55:18.9841524Z         
2025-03-14T04:55:18.9841923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:18.9842397Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T04:55:18.9842694Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T04:55:18.9842946Z         
2025-03-14T04:55:18.9843383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:18.9843971Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T04:55:18.9844277Z         
2025-03-14T04:55:18.9844696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:18.9845246Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T04:55:18.9845530Z         
2025-03-14T04:55:18.9846004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:18.9846629Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T04:55:18.9846914Z         
2025-03-14T04:55:18.9847481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:55:18.9848190Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:55:18.9848438Z         
2025-03-14T04:55:18.9848823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:18.9849314Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:55:18.9849564Z         
2025-03-14T04:55:18.9850088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:18.9850757Z         topk = l_pred_objectness_logits_0_.topk(1000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T04:55:18.9851094Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T04:55:18.9851369Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:55:18.9851844Z         
2025-03-14T04:55:18.9852432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:18.9853148Z         getitem_82: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:55:18.9853601Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_82, topk_idx)];  proposals_i_5 = getitem_82 = topk_idx = None
2025-03-14T04:55:18.9853949Z         
2025-03-14T04:55:18.9854501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:18.9855190Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:18.9855475Z         
2025-03-14T04:55:18.9855859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:18.9856338Z         to_6: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T04:55:18.9856576Z         
2025-03-14T04:55:18.9857098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:18.9857761Z         topk_1 = l_pred_objectness_logits_1_.topk(1000, dim = 1);  l_pred_objectness_logits_1_ = None
2025-03-14T04:55:18.9858098Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T04:55:18.9858383Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T04:55:18.9858621Z         
2025-03-14T04:55:18.9859173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:18.9859827Z         getitem_86: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:55:18.9860257Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_86, topk_idx_1)];  proposals_i_6 = getitem_86 = topk_idx_1 = None
2025-03-14T04:55:18.9860785Z         
2025-03-14T04:55:18.9861360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:18.9862051Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:18.9862340Z         
2025-03-14T04:55:18.9862731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:18.9863212Z         to_7: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T04:55:18.9863457Z         
2025-03-14T04:55:18.9863978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:18.9864634Z         topk_2 = l_pred_objectness_logits_2_.topk(1000, dim = 1);  l_pred_objectness_logits_2_ = None
2025-03-14T04:55:18.9864954Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T04:55:18.9865231Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T04:55:18.9865463Z         
2025-03-14T04:55:18.9866632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:18.9867269Z         getitem_90: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:55:18.9867724Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_90, topk_idx_2)];  proposals_i_7 = getitem_90 = topk_idx_2 = None
2025-03-14T04:55:18.9868071Z         
2025-03-14T04:55:18.9868630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:18.9869293Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:18.9869570Z         
2025-03-14T04:55:18.9869950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:18.9870419Z         to_8: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T04:55:18.9870659Z         
2025-03-14T04:55:18.9871174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:18.9871811Z         topk_3 = l_pred_objectness_logits_3_.topk(1000, dim = 1);  l_pred_objectness_logits_3_ = None
2025-03-14T04:55:18.9872128Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T04:55:18.9872395Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T04:55:18.9872621Z         
2025-03-14T04:55:18.9873159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:18.9873794Z         getitem_94: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:55:18.9874211Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_94, topk_idx_3)];  proposals_i_8 = getitem_94 = topk_idx_3 = None
2025-03-14T04:55:18.9874577Z         
2025-03-14T04:55:18.9875111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:18.9875771Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:18.9876049Z         
2025-03-14T04:55:18.9876428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:18.9876892Z         to_9: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T04:55:18.9877129Z         
2025-03-14T04:55:18.9877642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:18.9878291Z         topk_4 = l_pred_objectness_logits_4_.topk(1000, dim = 1);  l_pred_objectness_logits_4_ = None
2025-03-14T04:55:18.9878616Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T04:55:18.9878891Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T04:55:18.9879122Z         
2025-03-14T04:55:18.9879677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:18.9880348Z         getitem_98: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:55:18.9880811Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_98, topk_idx_4)];  proposals_i_9 = getitem_98 = topk_idx_4 = None
2025-03-14T04:55:18.9881153Z         
2025-03-14T04:55:18.9881712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:18.9882413Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:18.9882697Z         
2025-03-14T04:55:18.9883079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:18.9883560Z         to_10: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T04:55:18.9883802Z         
2025-03-14T04:55:18.9884167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:18.9884885Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T04:55:18.9885375Z         
2025-03-14T04:55:18.9885748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:18.9886571Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T04:55:18.9887166Z         
2025-03-14T04:55:18.9887538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:18.9888084Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_6, to_7, to_8, to_9, to_10], 0);  to_6 = to_7 = to_8 = to_9 = to_10 = level_ids = None
2025-03-14T04:55:18.9888428Z         
2025-03-14T04:55:18.9894878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:55:18.9895711Z         getitem_100: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T04:55:18.9895983Z         
2025-03-14T04:55:18.9896398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:55:18.9896898Z         tensor: "f32[5000, 4][4, 1]cpu" = getitem_100.to(torch.float32);  getitem_100 = None
2025-03-14T04:55:18.9897164Z         
2025-03-14T04:55:18.9897629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:55:18.9898198Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T04:55:18.9898448Z         
2025-03-14T04:55:18.9899015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:55:18.9899701Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T04:55:18.9900134Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:55:18.9900464Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:55:18.9900800Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:55:18.9901090Z         
2025-03-14T04:55:18.9901571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:55:18.9902110Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:55:18.9902347Z         
2025-03-14T04:55:44.5752357Z 
2025-03-14T04:55:44.5753207Z class GraphModule(torch.nn.Module):
2025-03-14T04:55:44.5759102Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T04:55:44.5761703Z         l_stack0_ = L_stack0_
2025-03-14T04:55:44.5763075Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:55:44.5763597Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:55:44.5764065Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:55:44.5764988Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:55:44.5765497Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:55:44.5766084Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:55:44.5766690Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:55:44.5768166Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:55:44.5768696Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:55:44.5769125Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:55:44.5769546Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:55:44.5769975Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:55:44.5770296Z         
2025-03-14T04:55:44.5770753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:55:44.5771307Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:55:44.5772340Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:55:44.5773246Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:55:44.5774118Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:55:44.5774932Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:55:44.5775240Z         
2025-03-14T04:55:44.5775689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:55:44.5776781Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:55:44.5777586Z         
2025-03-14T04:55:44.5778043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:55:44.5779177Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:55:44.5779931Z         
2025-03-14T04:55:44.5780301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:55:44.5780759Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.5781034Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:55:44.5781265Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:55:44.5781534Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.5781788Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T04:55:44.5782019Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:55:44.5782288Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.5782533Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T04:55:44.5782765Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T04:55:44.5783066Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.5783311Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T04:55:44.5783536Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T04:55:44.5783752Z         
2025-03-14T04:55:44.5784123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:44.5784900Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T04:55:44.5785444Z         
2025-03-14T04:55:44.5785907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:44.5786503Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:55:44.5786777Z         
2025-03-14T04:55:44.5787171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:44.5787724Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:55:44.5788020Z         
2025-03-14T04:55:44.5788417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:44.5788922Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:55:44.5789242Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:44.5789570Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T04:55:44.5789836Z         
2025-03-14T04:55:44.5790243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:44.5790743Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:55:44.5791052Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:55:44.5791385Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:55:44.5791687Z         
2025-03-14T04:55:44.5792184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:44.5792679Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:44.5792964Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T04:55:44.5793236Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T04:55:44.5793485Z         
2025-03-14T04:55:44.5793882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:44.5794428Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:55:44.5794740Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T04:55:44.5795032Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T04:55:44.5795285Z         
2025-03-14T04:55:44.5795713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:44.5796232Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:44.5796895Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T04:55:44.5797149Z         
2025-03-14T04:55:44.5797540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:44.5798049Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:44.5798369Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T04:55:44.5798600Z         
2025-03-14T04:55:44.5798986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:44.5799509Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:44.5799827Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T04:55:44.5800055Z         
2025-03-14T04:55:44.5800442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:44.5800994Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:55:44.5801353Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T04:55:44.5801582Z         
2025-03-14T04:55:44.5802004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:44.5802530Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:55:44.5802782Z         
2025-03-14T04:55:44.5803193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:44.5803706Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:55:44.5803954Z         
2025-03-14T04:55:44.5804384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:44.5804928Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:55:44.5805248Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T04:55:44.5805577Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:55:44.5805920Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T04:55:44.5806171Z         
2025-03-14T04:55:44.5806772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:44.5807324Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:55:44.5807665Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T04:55:44.5807991Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:55:44.5808333Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T04:55:44.5808587Z         
2025-03-14T04:55:44.5808997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:44.5809506Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:55:44.5809822Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:55:44.5810159Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T04:55:44.5810408Z         
2025-03-14T04:55:44.5810832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:44.5811355Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:55:44.5811816Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:55:44.5812185Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T04:55:44.5812479Z         
2025-03-14T04:55:44.5812905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:44.5813425Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:55:44.5813689Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:55:44.5813937Z         
2025-03-14T04:55:44.5815312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:44.5815778Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:55:44.5816034Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:55:44.5816272Z         
2025-03-14T04:55:44.5816847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:44.5817352Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:55:44.5817649Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:55:44.5817897Z         
2025-03-14T04:55:44.5818285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:44.5818758Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:55:44.5819044Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:55:44.5819288Z         
2025-03-14T04:55:44.5819719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:44.5820297Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:55:44.5820585Z         
2025-03-14T04:55:44.5820998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:44.5821553Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T04:55:44.5821872Z         
2025-03-14T04:55:44.5822326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:55:44.5823016Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T04:55:44.5823436Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T04:55:44.5823717Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T04:55:44.5824007Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T04:55:44.5824306Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T04:55:44.5824551Z         
2025-03-14T04:55:44.5824925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:55:44.5825474Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.5825813Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T04:55:44.5826051Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T04:55:44.5826717Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.5827109Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T04:55:44.5827380Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T04:55:44.5827746Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.5828675Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T04:55:44.5828922Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T04:55:44.5829315Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.5829657Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T04:55:44.5829886Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T04:55:44.5830098Z         
2025-03-14T04:55:44.5830524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:55:44.5831645Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:55:44.5832419Z         
2025-03-14T04:55:44.5832883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:55:44.5833556Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T04:55:44.5833981Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:55:44.5834268Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T04:55:44.5834562Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T04:55:44.5834864Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T04:55:44.5835501Z         
2025-03-14T04:55:44.5836068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:55:44.5836760Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:55:44.5837095Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:55:44.5837473Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:55:44.5837802Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:55:44.5838087Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:55:44.5838322Z         
2025-03-14T04:55:44.5838759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:55:44.5839279Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:55:44.5839515Z         
2025-03-14T04:55:44.5839652Z 
2025-03-14T04:55:44.5839741Z class GraphModule(torch.nn.Module):
2025-03-14T04:55:44.5841648Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T04:55:44.5843701Z         l_stack0_ = L_stack0_
2025-03-14T04:55:44.5844045Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:55:44.5844548Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:55:44.5845020Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:55:44.5845488Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:55:44.5846006Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:55:44.5846570Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:55:44.5847133Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:55:44.5847698Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:55:44.5848213Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:55:44.5848611Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:55:44.5849017Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:55:44.5849427Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:55:44.5849726Z         
2025-03-14T04:55:44.5850110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:55:44.5850608Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:55:44.5851388Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:55:44.5852707Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:55:44.5853451Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:55:44.5854153Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:55:44.5854428Z         
2025-03-14T04:55:44.5854828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:55:44.5855784Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:55:44.5856481Z         
2025-03-14T04:55:44.5856908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:55:44.5857890Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:55:44.5858663Z         
2025-03-14T04:55:44.5859033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:55:44.5859489Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.5859737Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:55:44.5859976Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:55:44.5860262Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.5860838Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T04:55:44.5861141Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:55:44.5861426Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.5861675Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T04:55:44.5861910Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T04:55:44.5862172Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.5862420Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T04:55:44.5862650Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T04:55:44.5862866Z         
2025-03-14T04:55:44.5863237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:44.5864017Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T04:55:44.5864559Z         
2025-03-14T04:55:44.5865042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:44.5865706Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:55:44.5865990Z         
2025-03-14T04:55:44.5866399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:44.5866985Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:55:44.5867273Z         
2025-03-14T04:55:44.5867695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:44.5868216Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:55:44.5868550Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:44.5868900Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T04:55:44.5869180Z         
2025-03-14T04:55:44.5869601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:44.5870124Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:55:44.5870450Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:55:44.5870859Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:55:44.5871148Z         
2025-03-14T04:55:44.5871559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:44.5872102Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:44.5872427Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T04:55:44.5872718Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T04:55:44.5872975Z         
2025-03-14T04:55:44.5873394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:44.5873939Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:55:44.5874255Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T04:55:44.5874548Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T04:55:44.5874806Z         
2025-03-14T04:55:44.5875225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:44.5875758Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:44.5876099Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T04:55:44.5876330Z         
2025-03-14T04:55:44.5876719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:44.5877220Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:44.5877542Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T04:55:44.5877773Z         
2025-03-14T04:55:44.5878151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:44.5878666Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:44.5879024Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T04:55:44.5879267Z         
2025-03-14T04:55:44.5879681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:44.5880210Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:55:44.5880550Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T04:55:44.5880778Z         
2025-03-14T04:55:44.5881193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:44.5881712Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:55:44.5881963Z         
2025-03-14T04:55:44.5882371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:44.5882880Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:55:44.5883122Z         
2025-03-14T04:55:44.5883542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:44.5884091Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:55:44.5884414Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T04:55:44.5884770Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:55:44.5885114Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T04:55:44.5885386Z         
2025-03-14T04:55:44.5885817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:44.5886375Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:55:44.5886712Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T04:55:44.5887058Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:55:44.5887417Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T04:55:44.5887679Z         
2025-03-14T04:55:44.5888124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:44.5888661Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:55:44.5889007Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:55:44.5889372Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T04:55:44.5889633Z         
2025-03-14T04:55:44.5890082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:44.5890611Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:55:44.5890962Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:55:44.5891349Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T04:55:44.5892016Z         
2025-03-14T04:55:44.5892480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:44.5892970Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:55:44.5893247Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:55:44.5893496Z         
2025-03-14T04:55:44.5893919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:44.5894392Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:55:44.5894652Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:55:44.5894891Z         
2025-03-14T04:55:44.5895286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:44.5895778Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:55:44.5896073Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:55:44.5896324Z         
2025-03-14T04:55:44.5896720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:44.5897666Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:55:44.5898309Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:55:44.5898568Z         
2025-03-14T04:55:44.5899006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:44.5899627Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:55:44.5899949Z         
2025-03-14T04:55:44.5900380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:44.5900945Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T04:55:44.5901630Z         
2025-03-14T04:55:44.5902140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:55:44.5902839Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T04:55:44.5903280Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T04:55:44.5903575Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T04:55:44.5903880Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T04:55:44.5904187Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T04:55:44.5904451Z         
2025-03-14T04:55:44.5905156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:55:44.5905803Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.5906157Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T04:55:44.5906398Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T04:55:44.5906772Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.5907118Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T04:55:44.5907388Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T04:55:44.5907755Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.5908102Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T04:55:44.5908338Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T04:55:44.5909112Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.5909751Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T04:55:44.5909992Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T04:55:44.5910214Z         
2025-03-14T04:55:44.5910645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:55:44.5911227Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:55:44.5911538Z         
2025-03-14T04:55:44.5912370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:55:44.5913069Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T04:55:44.5913482Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:55:44.5913798Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T04:55:44.5914098Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T04:55:44.5914431Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T04:55:44.5914688Z         
2025-03-14T04:55:44.5915272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:55:44.5915981Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:55:44.5916333Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:55:44.5916660Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:55:44.5916993Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:55:44.5917280Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:55:44.5917516Z         
2025-03-14T04:55:44.5917953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:55:44.5918653Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:55:44.5918883Z         
2025-03-14T04:55:44.5919020Z 
2025-03-14T04:55:44.5919107Z class GraphModule(torch.nn.Module):
2025-03-14T04:55:44.5920993Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T04:55:44.5923046Z         l_stack0_ = L_stack0_
2025-03-14T04:55:44.5923388Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:55:44.5923867Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:55:44.5924334Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:55:44.5924803Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:55:44.5925323Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:55:44.5925886Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:55:44.5926450Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:55:44.5927034Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:55:44.5927512Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:55:44.5927925Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:55:44.5928359Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:55:44.5928789Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:55:44.5929090Z         
2025-03-14T04:55:44.5929671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:55:44.5930167Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:55:44.5930907Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:55:44.5932577Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:55:44.5933378Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:55:44.5934113Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:55:44.5934397Z         
2025-03-14T04:55:44.5934813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:55:44.5935826Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:55:44.5936693Z         
2025-03-14T04:55:44.5937152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:55:44.5938146Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:55:44.5938874Z         
2025-03-14T04:55:44.5939250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:55:44.5939708Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.5939964Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:55:44.5940196Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:55:44.5940470Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.5940719Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T04:55:44.5940954Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:55:44.5941224Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.5941469Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T04:55:44.5941695Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T04:55:44.5941977Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.5942238Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T04:55:44.5942465Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T04:55:44.5942675Z         
2025-03-14T04:55:44.5943034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:44.5943847Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T04:55:44.5944393Z         
2025-03-14T04:55:44.5944846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:44.5945413Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:55:44.5945675Z         
2025-03-14T04:55:44.5946066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:44.5946826Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:55:44.5947110Z         
2025-03-14T04:55:44.5947512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:44.5948011Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:55:44.5948327Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:44.5948657Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T04:55:44.5948922Z         
2025-03-14T04:55:44.5949325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:44.5949828Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:55:44.5950140Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:55:44.5950499Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:55:44.5950769Z         
2025-03-14T04:55:44.5951160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:44.5951644Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:44.5951918Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T04:55:44.5952188Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T04:55:44.5952430Z         
2025-03-14T04:55:44.5952822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:44.5953333Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:55:44.5953637Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T04:55:44.5953912Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T04:55:44.5954154Z         
2025-03-14T04:55:44.5954553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:44.5955054Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:44.5955389Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T04:55:44.5955623Z         
2025-03-14T04:55:44.5956005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:44.5956667Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:44.5957018Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T04:55:44.5957254Z         
2025-03-14T04:55:44.5957647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:44.5958153Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:44.5958477Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T04:55:44.5958713Z         
2025-03-14T04:55:44.5959099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:44.5959636Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:55:44.5959981Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T04:55:44.5960207Z         
2025-03-14T04:55:44.5960971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:44.5961542Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:55:44.5961801Z         
2025-03-14T04:55:44.5962222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:44.5962748Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:55:44.5963000Z         
2025-03-14T04:55:44.5963434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:44.5964031Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:55:44.5964336Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T04:55:44.5964677Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:55:44.5965015Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T04:55:44.5965270Z         
2025-03-14T04:55:44.5965705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:44.5966238Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:55:44.5966555Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T04:55:44.5966880Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:55:44.5967226Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T04:55:44.5967482Z         
2025-03-14T04:55:44.5967907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:44.5968416Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:55:44.5968786Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:55:44.5969396Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T04:55:44.5969696Z         
2025-03-14T04:55:44.5970127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:44.5970681Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:55:44.5971026Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:55:44.5971387Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T04:55:44.5971846Z         
2025-03-14T04:55:44.5972404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:44.5972913Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:55:44.5973202Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:55:44.5973437Z         
2025-03-14T04:55:44.5973828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:44.5974281Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:55:44.5974534Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:55:44.5974764Z         
2025-03-14T04:55:44.5975147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:44.5975611Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:55:44.5975896Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:55:44.5976139Z         
2025-03-14T04:55:44.5976527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:44.5977691Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:55:44.5978030Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:55:44.5978283Z         
2025-03-14T04:55:44.5978729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:44.5979319Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:55:44.5979615Z         
2025-03-14T04:55:44.5980040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:44.5981167Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T04:55:44.5981469Z         
2025-03-14T04:55:44.5981945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:55:44.5982622Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T04:55:44.5983046Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T04:55:44.5983328Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T04:55:44.5983649Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T04:55:44.5983951Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T04:55:44.5984198Z         
2025-03-14T04:55:44.5984574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:55:44.5985169Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.5985645Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T04:55:44.5985890Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T04:55:44.5986744Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.5987119Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T04:55:44.5987363Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T04:55:44.5987736Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.5988086Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T04:55:44.5988324Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T04:55:44.5988689Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.5989030Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T04:55:44.5989262Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T04:55:44.5989876Z         
2025-03-14T04:55:44.5991027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:55:44.5991626Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:55:44.5991922Z         
2025-03-14T04:55:44.5992385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:55:44.5993505Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T04:55:44.5993951Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:55:44.5994291Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T04:55:44.5994589Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T04:55:44.5994893Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T04:55:44.5995152Z         
2025-03-14T04:55:44.5995721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:55:44.5996427Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:55:44.5996769Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:55:44.5997107Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:55:44.5997447Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:55:44.5997742Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:55:44.5997981Z         
2025-03-14T04:55:44.5998430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:55:44.5998960Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:55:44.5999197Z         
2025-03-14T04:55:44.5999358Z 
2025-03-14T04:55:44.5999449Z class GraphModule(torch.nn.Module):
2025-03-14T04:55:44.6001404Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T04:55:44.6003475Z         l_stack0_ = L_stack0_
2025-03-14T04:55:44.6003818Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:55:44.6004297Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:55:44.6004767Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:55:44.6005231Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:55:44.6005741Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:55:44.6006303Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:55:44.6006864Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:55:44.6007439Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:55:44.6007952Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:55:44.6008365Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:55:44.6008770Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:55:44.6009176Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:55:44.6009477Z         
2025-03-14T04:55:44.6009859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:55:44.6010343Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:55:44.6011083Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:55:44.6012385Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:55:44.6013214Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:55:44.6013982Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:55:44.6014269Z         
2025-03-14T04:55:44.6014698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:55:44.6015729Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:55:44.6016475Z         
2025-03-14T04:55:44.6016916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:55:44.6017970Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:55:44.6018732Z         
2025-03-14T04:55:44.6019105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:55:44.6019567Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.6019815Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:55:44.6020044Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:55:44.6020314Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.6020565Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T04:55:44.6020801Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:55:44.6021071Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.6021316Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T04:55:44.6021549Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T04:55:44.6021813Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:44.6022080Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T04:55:44.6022307Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T04:55:44.6022509Z         
2025-03-14T04:55:44.6022879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:44.6023645Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T04:55:44.6024197Z         
2025-03-14T04:55:44.6024642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:44.6025210Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:55:44.6025480Z         
2025-03-14T04:55:44.6025869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:44.6026383Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:55:44.6026654Z         
2025-03-14T04:55:44.6027047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:44.6027609Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:55:44.6027945Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:44.6028327Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T04:55:44.6028610Z         
2025-03-14T04:55:44.6029034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:44.6029539Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:55:44.6029847Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:55:44.6030185Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:55:44.6030459Z         
2025-03-14T04:55:44.6030856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:44.6031346Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:44.6031624Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T04:55:44.6031896Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T04:55:44.6032140Z         
2025-03-14T04:55:44.6032549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:44.6033087Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:55:44.6033405Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T04:55:44.6033698Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T04:55:44.6033957Z         
2025-03-14T04:55:44.6034381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:44.6034916Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:44.6035277Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T04:55:44.6035519Z         
2025-03-14T04:55:44.6035922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:44.6036524Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:44.6036873Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T04:55:44.6037123Z         
2025-03-14T04:55:44.6037528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:44.6038054Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:44.6038393Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T04:55:44.6038639Z         
2025-03-14T04:55:44.6039043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:44.6039600Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:55:44.6039957Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T04:55:44.6040198Z         
2025-03-14T04:55:44.6040656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:44.6041211Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:55:44.6041493Z         
2025-03-14T04:55:44.6041927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:44.6042490Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:55:44.6042756Z         
2025-03-14T04:55:44.6043212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:44.6043787Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:55:44.6044127Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T04:55:44.6044481Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:55:44.6044849Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T04:55:44.6045122Z         
2025-03-14T04:55:44.6045576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:44.6046148Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:55:44.6046481Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T04:55:44.6046829Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:55:44.6047190Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T04:55:44.6047459Z         
2025-03-14T04:55:44.6047902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:44.6048439Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:55:44.6048802Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:55:44.6049159Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T04:55:44.6049418Z         
2025-03-14T04:55:44.6049853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:44.6050376Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:55:44.6050724Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:55:44.6051090Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T04:55:44.6051372Z         
2025-03-14T04:55:44.6052205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:44.6052758Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:55:44.6053027Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:55:44.6053269Z         
2025-03-14T04:55:44.6053670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:44.6054142Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:55:44.6054452Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:55:44.6054708Z         
2025-03-14T04:55:44.6055138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:44.6055686Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:55:44.6056026Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:55:44.6056296Z         
2025-03-14T04:55:44.6056725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:44.6057248Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:55:44.6057566Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:55:44.6057835Z         
2025-03-14T04:55:44.6058313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:44.6058963Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:55:44.6059281Z         
2025-03-14T04:55:44.6059746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:44.6060305Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T04:55:44.6060990Z         
2025-03-14T04:55:44.6061471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:55:44.6062163Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T04:55:44.6062589Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T04:55:44.6062877Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T04:55:44.6063171Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T04:55:44.6063532Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T04:55:44.6063772Z         
2025-03-14T04:55:44.6064146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:55:44.6064699Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.6065046Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T04:55:44.6065288Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T04:55:44.6065653Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.6065995Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T04:55:44.6066231Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T04:55:44.6066597Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.6066934Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T04:55:44.6067163Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T04:55:44.6067517Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:55:44.6067851Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T04:55:44.6068076Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T04:55:44.6068319Z         
2025-03-14T04:55:44.6068740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:55:44.6069322Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:55:44.6069608Z         
2025-03-14T04:55:44.6070069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:55:44.6070749Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T04:55:44.6071159Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:55:44.6071444Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T04:55:44.6071741Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T04:55:44.6072041Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T04:55:44.6072291Z         
2025-03-14T04:55:44.6072858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:55:44.6073569Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:55:44.6073905Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:55:44.6074236Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:55:44.6074572Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:55:44.6074867Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:55:44.6075098Z         
2025-03-14T04:55:44.6075533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:55:44.6076055Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:55:44.6076301Z         
2025-03-14T04:55:46.3274083Z 
2025-03-14T04:55:46.3282460Z class GraphModule(torch.nn.Module):
2025-03-14T04:55:46.3284143Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T04:55:46.3285077Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:55:46.3285315Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:55:46.3285640Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:55:46.3286062Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:55:46.3286477Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:55:46.3286884Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:55:46.3287186Z         
2025-03-14T04:55:46.3287631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:55:46.3288131Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:46.3288413Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:55:46.3288937Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:55:46.3289233Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:46.3289501Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T04:55:46.3289857Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:55:46.3290136Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:46.3290397Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T04:55:46.3290692Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T04:55:46.3290973Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:46.3291231Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T04:55:46.3291597Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T04:55:46.3291836Z         
2025-03-14T04:55:46.3292237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:46.3293067Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T04:55:46.3293662Z         
2025-03-14T04:55:46.3294154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:46.3294764Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:55:46.3295050Z         
2025-03-14T04:55:46.3295474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:46.3296035Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:55:46.3296328Z         
2025-03-14T04:55:46.3296748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:46.3297276Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:55:46.3297659Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:46.3298008Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T04:55:46.3298288Z         
2025-03-14T04:55:46.3298714Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:46.3299240Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:55:46.3299567Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:55:46.3299926Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:55:46.3300216Z         
2025-03-14T04:55:46.3300629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:46.3301145Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:46.3301443Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T04:55:46.3301732Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T04:55:46.3301992Z         
2025-03-14T04:55:46.3302411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:46.3302973Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:55:46.3303296Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T04:55:46.3303616Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T04:55:46.3303877Z         
2025-03-14T04:55:46.3304372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:46.3304922Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:46.3305272Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T04:55:46.3305518Z         
2025-03-14T04:55:46.3305929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:46.3306460Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:46.3306793Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T04:55:46.3307036Z         
2025-03-14T04:55:46.3307444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:46.3307970Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:46.3308298Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T04:55:46.3308524Z         
2025-03-14T04:55:46.3308925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:46.3309475Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:55:46.3309831Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T04:55:46.3310065Z         
2025-03-14T04:55:46.3310498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:46.3311055Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:55:46.3311316Z         
2025-03-14T04:55:46.3311738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:46.3312263Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:55:46.3312518Z         
2025-03-14T04:55:46.3312952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:46.3313500Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:55:46.3313826Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T04:55:46.3314163Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:55:46.3314514Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T04:55:46.3314773Z         
2025-03-14T04:55:46.3315211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:46.3315760Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:55:46.3317363Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T04:55:46.3317710Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:55:46.3318201Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T04:55:46.3318463Z         
2025-03-14T04:55:46.3318912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:46.3319431Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:55:46.3319770Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:55:46.3320127Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T04:55:46.3320384Z         
2025-03-14T04:55:46.3320815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:46.3321326Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:55:46.3321670Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:55:46.3322034Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T04:55:46.3322293Z         
2025-03-14T04:55:46.3322704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:46.3323175Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:55:46.3323442Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:55:46.3323680Z         
2025-03-14T04:55:46.3324084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:46.3324546Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:55:46.3324806Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:55:46.3325043Z         
2025-03-14T04:55:46.3325460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:46.3325947Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:55:46.3326240Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:55:46.3326493Z         
2025-03-14T04:55:46.3326879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:46.3327357Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:55:46.3327643Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:55:46.3327893Z         
2025-03-14T04:55:46.3328334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:46.3328935Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:55:46.3329230Z         
2025-03-14T04:55:46.3329656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:46.3330218Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T04:55:46.3330511Z         
2025-03-14T04:55:46.3330990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:55:46.3331838Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T04:55:46.3332344Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T04:55:46.3332680Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T04:55:46.3333000Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T04:55:46.3333312Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T04:55:46.3333575Z         
2025-03-14T04:55:46.3333978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:55:46.3334541Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T04:55:46.3334894Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T04:55:46.3335138Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T04:55:46.3335504Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:55:46.3335850Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T04:55:46.3336085Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T04:55:46.3336445Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:55:46.3336783Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T04:55:46.3337018Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T04:55:46.3337376Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:55:46.3337712Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T04:55:46.3337940Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T04:55:46.3338158Z         
2025-03-14T04:55:46.3338574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:55:46.3339201Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:55:46.3339524Z         
2025-03-14T04:55:46.3339962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:55:46.3340629Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T04:55:46.3341048Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:55:46.3341331Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T04:55:46.3341623Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T04:55:46.3341919Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T04:55:46.3342167Z         
2025-03-14T04:55:46.3342712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:55:46.3343398Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:55:46.3343732Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:55:46.3344075Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:55:46.3344406Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:55:46.3344701Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:55:46.3344937Z         
2025-03-14T04:55:46.3345388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:55:46.3345910Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:55:46.3346141Z         
2025-03-14T04:55:46.3349830Z 
2025-03-14T04:55:46.3350392Z class GraphModule(torch.nn.Module):
2025-03-14T04:55:46.3351365Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T04:55:46.3352282Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:55:46.3352509Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:55:46.3352866Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:55:46.3353286Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:55:46.3353691Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:55:46.3354091Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:55:46.3354447Z         
2025-03-14T04:55:46.3354881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:55:46.3355455Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:46.3355718Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:55:46.3356009Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:55:46.3356304Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:46.3356687Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T04:55:46.3356985Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:55:46.3357264Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:46.3357568Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T04:55:46.3357830Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T04:55:46.3358108Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:46.3358353Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T04:55:46.3358576Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T04:55:46.3358788Z         
2025-03-14T04:55:46.3359177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:46.3360055Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T04:55:46.3361371Z         
2025-03-14T04:55:46.3361935Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:46.3362539Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:55:46.3362814Z         
2025-03-14T04:55:46.3363326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:46.3363868Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:55:46.3364195Z         
2025-03-14T04:55:46.3364598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:46.3365152Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:55:46.3365474Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:46.3365808Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T04:55:46.3366077Z         
2025-03-14T04:55:46.3367140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:46.3367657Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:55:46.3367972Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:55:46.3368313Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:55:46.3368588Z         
2025-03-14T04:55:46.3368985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:46.3369474Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:46.3369754Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T04:55:46.3370027Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T04:55:46.3370278Z         
2025-03-14T04:55:46.3370927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:46.3371597Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:55:46.3371972Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T04:55:46.3372274Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T04:55:46.3372543Z         
2025-03-14T04:55:46.3372976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:46.3373512Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:46.3373849Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T04:55:46.3374089Z         
2025-03-14T04:55:46.3374491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:46.3375017Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:46.3375355Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T04:55:46.3375597Z         
2025-03-14T04:55:46.3375998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:46.3376687Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:46.3377033Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T04:55:46.3377280Z         
2025-03-14T04:55:46.3377725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:46.3378532Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:55:46.3378936Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T04:55:46.3379188Z         
2025-03-14T04:55:46.3379665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:46.3380230Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:55:46.3380499Z         
2025-03-14T04:55:46.3380947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:46.3381504Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:55:46.3381768Z         
2025-03-14T04:55:46.3382229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:46.3382809Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:55:46.3383148Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T04:55:46.3383501Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:55:46.3383863Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T04:55:46.3384136Z         
2025-03-14T04:55:46.3384602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:46.3385182Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:55:46.3385505Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T04:55:46.3385839Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:55:46.3386211Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T04:55:46.3386624Z         
2025-03-14T04:55:46.3387055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:46.3387573Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:55:46.3388149Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:55:46.3388522Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T04:55:46.3388776Z         
2025-03-14T04:55:46.3389198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:46.3389702Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:55:46.3390037Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:55:46.3390386Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T04:55:46.3390639Z         
2025-03-14T04:55:46.3391038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:46.3391521Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:55:46.3391782Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:55:46.3392018Z         
2025-03-14T04:55:46.3392430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:46.3392893Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:55:46.3393168Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:55:46.3393402Z         
2025-03-14T04:55:46.3393798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:46.3394283Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:55:46.3394583Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:55:46.3394833Z         
2025-03-14T04:55:46.3395224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:46.3395707Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:55:46.3395997Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:55:46.3396249Z         
2025-03-14T04:55:46.3396838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:46.3397422Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:55:46.3397713Z         
2025-03-14T04:55:46.3398131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:46.3398683Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T04:55:46.3398972Z         
2025-03-14T04:55:46.3399414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:55:46.3400399Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T04:55:46.3400843Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T04:55:46.3401144Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T04:55:46.3401447Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T04:55:46.3401757Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T04:55:46.3402016Z         
2025-03-14T04:55:46.3402406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:55:46.3402975Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T04:55:46.3403322Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T04:55:46.3403568Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T04:55:46.3403943Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:55:46.3404288Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T04:55:46.3404528Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T04:55:46.3404895Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:55:46.3405262Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T04:55:46.3405502Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T04:55:46.3405869Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:55:46.3406233Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T04:55:46.3406615Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T04:55:46.3406835Z         
2025-03-14T04:55:46.3407278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:55:46.3407873Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:55:46.3408193Z         
2025-03-14T04:55:46.3408633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:55:46.3409779Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T04:55:46.3410243Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:55:46.3410544Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T04:55:46.3410848Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T04:55:46.3411155Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T04:55:46.3411476Z         
2025-03-14T04:55:46.3412068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:55:46.3412799Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:55:46.3413142Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:55:46.3413486Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:55:46.3413827Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:55:46.3414164Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:55:46.3414404Z         
2025-03-14T04:55:46.3414854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:55:46.3415380Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:55:46.3415615Z         
2025-03-14T04:55:46.3425569Z 
2025-03-14T04:55:46.3426120Z class GraphModule(torch.nn.Module):
2025-03-14T04:55:46.3427056Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T04:55:46.3427969Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:55:46.3428218Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:55:46.3428538Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:55:46.3428945Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:55:46.3429358Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:55:46.3429895Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:55:46.3430200Z         
2025-03-14T04:55:46.3430604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:55:46.3431134Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:46.3431392Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:55:46.3431669Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:55:46.3431967Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:46.3432222Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T04:55:46.3432461Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:55:46.3432786Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:46.3433039Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T04:55:46.3433276Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T04:55:46.3433543Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:55:46.3433797Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T04:55:46.3434029Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T04:55:46.3434254Z         
2025-03-14T04:55:46.3434645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:55:46.3435460Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T04:55:46.3436027Z         
2025-03-14T04:55:46.3436506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:46.3437107Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:55:46.3437383Z         
2025-03-14T04:55:46.3437792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:46.3438372Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:55:46.3438652Z         
2025-03-14T04:55:46.3439060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:46.3439568Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:55:46.3439893Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:46.3440229Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T04:55:46.3440499Z         
2025-03-14T04:55:46.3440907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:46.3441429Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:55:46.3441756Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:55:46.3442095Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:55:46.3442371Z         
2025-03-14T04:55:46.3442776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:46.3443293Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:46.3443573Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T04:55:46.3443846Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T04:55:46.3444108Z         
2025-03-14T04:55:46.3444503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:46.3445038Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:55:46.3445339Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T04:55:46.3445615Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T04:55:46.3445862Z         
2025-03-14T04:55:46.3446266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:46.3446825Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:46.3447144Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T04:55:46.3447375Z         
2025-03-14T04:55:46.3447759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:46.3448256Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:46.3448573Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T04:55:46.3448805Z         
2025-03-14T04:55:46.3449187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:46.3449682Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:46.3449997Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T04:55:46.3450221Z         
2025-03-14T04:55:46.3450603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:46.3451160Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:55:46.3451564Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T04:55:46.3451803Z         
2025-03-14T04:55:46.3462033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:46.3462762Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:55:46.3463039Z         
2025-03-14T04:55:46.3463492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:46.3464034Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:55:46.3464297Z         
2025-03-14T04:55:46.3464751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:46.3465315Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:55:46.3465652Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T04:55:46.3465997Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:55:46.3466499Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T04:55:46.3466772Z         
2025-03-14T04:55:46.3467231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:46.3467832Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:55:46.3468208Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T04:55:46.3468556Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:55:46.3468910Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T04:55:46.3469167Z         
2025-03-14T04:55:46.3469601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:46.3470110Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:55:46.3470453Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:55:46.3470802Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T04:55:46.3471055Z         
2025-03-14T04:55:46.3471474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:46.3471972Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:55:46.3472301Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:55:46.3472653Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T04:55:46.3472911Z         
2025-03-14T04:55:46.3473319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:46.3473791Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:55:46.3474059Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:55:46.3474343Z         
2025-03-14T04:55:46.3474762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:46.3475222Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:55:46.3475483Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:55:46.3475715Z         
2025-03-14T04:55:46.3476107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:46.3476588Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:55:46.3476883Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:55:46.3477146Z         
2025-03-14T04:55:46.3477534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:46.3478011Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:55:46.3478294Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:55:46.3478536Z         
2025-03-14T04:55:46.3478976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:46.3479606Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:55:46.3479901Z         
2025-03-14T04:55:46.3480310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:46.3480887Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T04:55:46.3481198Z         
2025-03-14T04:55:46.3481648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:55:46.3482337Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T04:55:46.3482765Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T04:55:46.3483053Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T04:55:46.3483349Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T04:55:46.3483653Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T04:55:46.3483901Z         
2025-03-14T04:55:46.3484282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:55:46.3484844Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T04:55:46.3485189Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T04:55:46.3485433Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T04:55:46.3485796Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:55:46.3486139Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T04:55:46.3486377Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T04:55:46.3486734Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:55:46.3487072Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T04:55:46.3487310Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T04:55:46.3487693Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:55:46.3488030Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T04:55:46.3488257Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T04:55:46.3488469Z         
2025-03-14T04:55:46.3488883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:55:46.3489475Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:55:46.3489792Z         
2025-03-14T04:55:46.3490237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:55:46.3490912Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T04:55:46.3491341Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:55:46.3491758Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T04:55:46.3492080Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T04:55:46.3492419Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T04:55:46.3492680Z         
2025-03-14T04:55:46.3493271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:55:46.3494006Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:55:46.3494340Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:55:46.3494682Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:55:46.3495018Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:55:46.3495304Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:55:46.3495538Z         
2025-03-14T04:55:46.3495977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:55:46.3496492Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:55:46.3496725Z         
2025-03-14T04:55:48.8845201Z 
2025-03-14T04:55:48.8848465Z class GraphModule(torch.nn.Module):
2025-03-14T04:55:48.8848941Z     def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"):
2025-03-14T04:55:48.8849296Z         l_scores_0_ = L_scores_0_
2025-03-14T04:55:48.8849541Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:55:48.8849745Z         
2025-03-14T04:55:48.8850371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:55:48.8851110Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:55:48.8851579Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:55:48.8851970Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:55:48.8852334Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:55:48.8852655Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:55:48.8853243Z         
2025-03-14T04:55:48.8853714Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:55:48.8854263Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:55:48.8854513Z         
2025-03-14T04:55:48.8854600Z 
2025-03-14T04:55:48.8854700Z class GraphModule(torch.nn.Module):
2025-03-14T04:55:48.8855043Z     def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"):
2025-03-14T04:55:48.8855373Z         l_scores_0_ = L_scores_0_
2025-03-14T04:55:48.8855582Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:55:48.8855775Z         
2025-03-14T04:55:48.8856356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:55:48.8857040Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:55:48.8857368Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:55:48.8857690Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:55:48.8858007Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:55:48.8858302Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:55:48.8858603Z         
2025-03-14T04:55:48.8859150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:55:48.8859740Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:55:48.8859977Z         
2025-03-14T04:56:07.0497577Z Compilation time (from dynamo_timed): 81.658758581
2025-03-14T04:56:07.0497985Z pass
2025-03-14T04:56:07.0503165Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:56:07.0504029Z TIMING: entire_frame_compile:81.65876 gc:0.04713 _recursive_pre_grad_passes:0.03905 async_compile.wait:31.99995 backend_compile:61.88275 _recursive_joint_graph_passes:0.31343 _recursive_post_grad_passes:0.24504 code_gen:41.0179 inductor_compile:46.49378 total_wall_time:81.65876
2025-03-14T04:56:07.0505122Z STATS: call_* op count: 1160 | FakeTensorMode.__torch_dispatch__:42549 | FakeTensor.__torch_dispatch__:4401 | ProxyTorchDispatchMode.__torch_dispatch__:13994 | attempt fast:202 | slow no contiguity match:72 | fast is_contiguous:130
2025-03-14T04:56:07.0505828Z Dynamo produced 61 graphs covering 1160 ops with 46 graph breaks (6 unique)
2025-03-14T04:56:13.1688030Z 
2025-03-14T04:56:24.8569346Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:56:24.8569670Z loading model: 0it [00:11, ?it/s]
2025-03-14T04:56:24.8581700Z cpu  eval  detectron2_fasterrcnn_r_50_c4      
2025-03-14T04:56:32.3578206Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_50_c4. Setting accuracy check to cosine
2025-03-14T04:56:32.3703390Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:56:53.2780626Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:57:12.7834217Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:57:21.7998486Z 
2025-03-14T04:57:21.7999391Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:21.8052956Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:57:21.8096343Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:57:21.8096893Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8097713Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8098652Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8099528Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8100267Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8100989Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8101781Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8102644Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8103472Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8104296Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8105016Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8105840Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8106708Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8107588Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8108372Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8109079Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8109822Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8110605Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8111350Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8112075Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8112776Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:21.8113518Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8114294Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8115028Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:21.8115762Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:21.8116455Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8117176Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8117934Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8118646Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8119360Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8120036Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8120733Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8121466Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8122187Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8122878Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8123531Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8124215Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8124951Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8125667Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8126352Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8127002Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8127706Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8128487Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8129244Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8129969Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8130666Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8131454Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8132287Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8133095Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8133822Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8134532Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8135273Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8136045Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8136796Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8137519Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8138208Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8138930Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8139707Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8140467Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8141192Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8141909Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8142632Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8143412Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8144178Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8144869Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8145524Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8146206Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8146937Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8147672Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8148374Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8149054Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:21.8149771Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8150535Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8151270Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:21.8151985Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:21.8152651Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8153335Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8154069Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8154785Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8155473Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8156158Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8156833Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8157566Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8158288Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8158992Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8159659Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8160351Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8161250Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8161997Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8162750Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8163412Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8164095Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8164842Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8165560Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8166280Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8166966Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8167699Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8168477Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8169246Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8170062Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8170794Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8171599Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8172431Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8173263Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8174023Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8174737Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8175517Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8176309Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8177107Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8177895Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8178604Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8179333Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8180126Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8180890Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8181619Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8182305Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8183041Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8183773Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8184487Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8185197Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8185854Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8186536Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8187271Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8187987Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8188681Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8189332Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8190060Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8190809Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8191573Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8192283Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8192956Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8193659Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8194422Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8195166Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8195874Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8196570Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:21.8197277Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8198042Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8198802Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:21.8199519Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:21.8200183Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8200863Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8201598Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8202322Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8203015Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8203693Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8204388Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8205152Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8205873Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8206599Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8207295Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8208007Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8208783Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8209534Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8210254Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8210943Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8211791Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8212686Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8213472Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8214270Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8215039Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8215842Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8216718Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8217550Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8218392Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8219162Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8219986Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8220813Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8221539Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8222225Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8222878Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8223561Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8224305Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8225017Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8225699Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8226347Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8227029Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8227784Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8228498Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8229186Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8229845Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8230535Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8231316Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8232033Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8232736Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8233389Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8234101Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8234837Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8235541Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8236228Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8236898Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8237589Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8238320Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8239030Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8239714Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8240402Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8241168Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8241921Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8242637Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8243330Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8243984Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8244673Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8245452Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8246230Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8246952Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8247659Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8248404Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8249235Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8250053Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8250832Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8251651Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8252436Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8253269Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8254047Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8254789Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8255583Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:57:21.8256382Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:57:21.8257128Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:57:21.8257926Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:57:21.8258750Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:57:21.8259585Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:57:21.8260331Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:57:21.8260952Z         
2025-03-14T04:57:21.8261351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8262190Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8262819Z         
2025-03-14T04:57:21.8263186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8265051Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8266684Z         
2025-03-14T04:57:21.8267060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:57:21.8267535Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:57:21.8267791Z         
2025-03-14T04:57:21.8268228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:57:21.8268908Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:57:21.8269274Z         
2025-03-14T04:57:21.8269610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8270342Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8270914Z         
2025-03-14T04:57:21.8271261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8273169Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8274902Z         
2025-03-14T04:57:21.8275295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8275806Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:57:21.8276098Z         
2025-03-14T04:57:21.8276456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8277241Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8277861Z         
2025-03-14T04:57:21.8278237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8280224Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8281964Z         
2025-03-14T04:57:21.8282355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8282866Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:57:21.8283145Z         
2025-03-14T04:57:21.8283501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8284296Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8284893Z         
2025-03-14T04:57:21.8285260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8287253Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8289031Z         
2025-03-14T04:57:21.8289387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8290179Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:21.8290780Z         
2025-03-14T04:57:21.8291148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8293425Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8295352Z         
2025-03-14T04:57:21.8295768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8296324Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:57:21.8296624Z         
2025-03-14T04:57:21.8297036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8297600Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:57:21.8297902Z         
2025-03-14T04:57:21.8298284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8299124Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8299742Z         
2025-03-14T04:57:21.8300106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8302121Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8303877Z         
2025-03-14T04:57:21.8304271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8304782Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:57:21.8305060Z         
2025-03-14T04:57:21.8305411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8306207Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8306809Z         
2025-03-14T04:57:21.8307175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8309182Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8310924Z         
2025-03-14T04:57:21.8311323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8311841Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:57:21.8312119Z         
2025-03-14T04:57:21.8312477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8313266Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8313819Z         
2025-03-14T04:57:21.8314176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8316069Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8317819Z         
2025-03-14T04:57:21.8318181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8318671Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:57:21.8318941Z         
2025-03-14T04:57:21.8319303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8319790Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:57:21.8320057Z         
2025-03-14T04:57:21.8320409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8321159Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8321711Z         
2025-03-14T04:57:21.8322077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8323933Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8325713Z         
2025-03-14T04:57:21.8326126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8326635Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:57:21.8326904Z         
2025-03-14T04:57:21.8327260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8328099Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8328712Z         
2025-03-14T04:57:21.8329099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8331188Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8333080Z         
2025-03-14T04:57:21.8333499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8334017Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:57:21.8334296Z         
2025-03-14T04:57:21.8334660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8335491Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8336109Z         
2025-03-14T04:57:21.8336482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8338508Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8340304Z         
2025-03-14T04:57:21.8340696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8341223Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:57:21.8341515Z         
2025-03-14T04:57:21.8341869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8342357Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:57:21.8342623Z         
2025-03-14T04:57:21.8342954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8343688Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8344294Z         
2025-03-14T04:57:21.8344651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8346518Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8348141Z         
2025-03-14T04:57:21.8348507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8348984Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:57:21.8349250Z         
2025-03-14T04:57:21.8349590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8350337Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8350886Z         
2025-03-14T04:57:21.8351239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8353046Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8354680Z         
2025-03-14T04:57:21.8355046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8355539Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:57:21.8355794Z         
2025-03-14T04:57:21.8356116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8356837Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8357371Z         
2025-03-14T04:57:21.8357729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8359577Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8361387Z         
2025-03-14T04:57:21.8361793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8362584Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:21.8363154Z         
2025-03-14T04:57:21.8363562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8365564Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8367343Z         
2025-03-14T04:57:21.8367719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8368220Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:57:21.8368493Z         
2025-03-14T04:57:21.8368882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8369403Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:57:21.8369696Z         
2025-03-14T04:57:21.8370063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8370882Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8371536Z         
2025-03-14T04:57:21.8371928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8373931Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8375605Z         
2025-03-14T04:57:21.8375991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8376496Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:57:21.8376770Z         
2025-03-14T04:57:21.8377124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8377886Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8378448Z         
2025-03-14T04:57:21.8378831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8380813Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8382547Z         
2025-03-14T04:57:21.8382933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8383442Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:57:21.8383713Z         
2025-03-14T04:57:21.8384061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8384840Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8385420Z         
2025-03-14T04:57:21.8385782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8387669Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8389327Z         
2025-03-14T04:57:21.8389687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8390172Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:57:21.8390444Z         
2025-03-14T04:57:21.8390807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8391292Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:57:21.8391562Z         
2025-03-14T04:57:21.8391910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8392641Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8393202Z         
2025-03-14T04:57:21.8393564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8395438Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8397161Z         
2025-03-14T04:57:21.8397549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8398051Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:57:21.8398318Z         
2025-03-14T04:57:21.8398663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8399428Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8400001Z         
2025-03-14T04:57:21.8400372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8402289Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8403943Z         
2025-03-14T04:57:21.8404312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8404793Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:57:21.8405056Z         
2025-03-14T04:57:21.8405388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8406144Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8406735Z         
2025-03-14T04:57:21.8407111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8409102Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8410850Z         
2025-03-14T04:57:21.8411234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8411812Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:57:21.8412114Z         
2025-03-14T04:57:21.8412530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8413021Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:57:21.8413293Z         
2025-03-14T04:57:21.8413624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8414356Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8414912Z         
2025-03-14T04:57:21.8415263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8417191Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8418850Z         
2025-03-14T04:57:21.8419214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8419690Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:57:21.8419952Z         
2025-03-14T04:57:21.8420297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8421030Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8421598Z         
2025-03-14T04:57:21.8421958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8423831Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8425496Z         
2025-03-14T04:57:21.8425871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8426360Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:57:21.8426618Z         
2025-03-14T04:57:21.8426957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8427714Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8428274Z         
2025-03-14T04:57:21.8428634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8430504Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8432138Z         
2025-03-14T04:57:21.8432500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8432981Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:57:21.8433256Z         
2025-03-14T04:57:21.8433623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8434131Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:57:21.8434403Z         
2025-03-14T04:57:21.8434737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8435541Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8436100Z         
2025-03-14T04:57:21.8436470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8438385Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8440118Z         
2025-03-14T04:57:21.8440501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8441001Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:57:21.8441304Z         
2025-03-14T04:57:21.8441808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8442889Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8443814Z         
2025-03-14T04:57:21.8444393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8447068Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8449379Z         
2025-03-14T04:57:21.8449872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8450377Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:57:21.8450649Z         
2025-03-14T04:57:21.8451107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8451968Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8452578Z         
2025-03-14T04:57:21.8452975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8454859Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8456515Z         
2025-03-14T04:57:21.8456855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8457616Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:21.8458183Z         
2025-03-14T04:57:21.8458539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8460501Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8462382Z         
2025-03-14T04:57:21.8462751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8463242Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:57:21.8463509Z         
2025-03-14T04:57:21.8463885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8464381Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:57:21.8464648Z         
2025-03-14T04:57:21.8464989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8465771Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8466350Z         
2025-03-14T04:57:21.8466712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8468578Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8470219Z         
2025-03-14T04:57:21.8470592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8471071Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:57:21.8471328Z         
2025-03-14T04:57:21.8471657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8472389Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8472941Z         
2025-03-14T04:57:21.8473298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8475214Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8476919Z         
2025-03-14T04:57:21.8477299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8477793Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:57:21.8478061Z         
2025-03-14T04:57:21.8478408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8479190Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8479768Z         
2025-03-14T04:57:21.8480138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8482089Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8483746Z         
2025-03-14T04:57:21.8484100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8484576Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:57:21.8484834Z         
2025-03-14T04:57:21.8485192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8485693Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:57:21.8485964Z         
2025-03-14T04:57:21.8486308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8487061Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8487677Z         
2025-03-14T04:57:21.8488067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8490134Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8492027Z         
2025-03-14T04:57:21.8492461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8492987Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:57:21.8493250Z         
2025-03-14T04:57:21.8493583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8494333Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8494909Z         
2025-03-14T04:57:21.8495246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8497207Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8498908Z         
2025-03-14T04:57:21.8499276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8499739Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:57:21.8499988Z         
2025-03-14T04:57:21.8500315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8501034Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8501567Z         
2025-03-14T04:57:21.8501908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8503767Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8505392Z         
2025-03-14T04:57:21.8505754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8506232Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:57:21.8506492Z         
2025-03-14T04:57:21.8506855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8507329Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:57:21.8507587Z         
2025-03-14T04:57:21.8507933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8508657Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8509208Z         
2025-03-14T04:57:21.8509567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8511444Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8513078Z         
2025-03-14T04:57:21.8513440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8513911Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:57:21.8514165Z         
2025-03-14T04:57:21.8514493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8515224Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8515774Z         
2025-03-14T04:57:21.8516122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8517947Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8519564Z         
2025-03-14T04:57:21.8519931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8520398Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:57:21.8520650Z         
2025-03-14T04:57:21.8520978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8521723Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8522278Z         
2025-03-14T04:57:21.8522623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8524471Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8526082Z         
2025-03-14T04:57:21.8526443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8526919Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:57:21.8527192Z         
2025-03-14T04:57:21.8527552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8528026Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:57:21.8528280Z         
2025-03-14T04:57:21.8528606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8529323Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8529872Z         
2025-03-14T04:57:21.8530220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8532199Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8533875Z         
2025-03-14T04:57:21.8534240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8534711Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:57:21.8534962Z         
2025-03-14T04:57:21.8535315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8536038Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8536592Z         
2025-03-14T04:57:21.8536959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8538746Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8540337Z         
2025-03-14T04:57:21.8540683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8541143Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:57:21.8541376Z         
2025-03-14T04:57:21.8541699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8542408Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8542967Z         
2025-03-14T04:57:21.8543298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8545127Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8546758Z         
2025-03-14T04:57:21.8547124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8547603Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:57:21.8547861Z         
2025-03-14T04:57:21.8548224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8548726Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:57:21.8548987Z         
2025-03-14T04:57:21.8549322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8550065Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8550596Z         
2025-03-14T04:57:21.8550938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8552767Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8554383Z         
2025-03-14T04:57:21.8554748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8555216Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:57:21.8555465Z         
2025-03-14T04:57:21.8555792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8556513Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8557060Z         
2025-03-14T04:57:21.8557403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8559230Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8561031Z         
2025-03-14T04:57:21.8561422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8561917Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:57:21.8562170Z         
2025-03-14T04:57:21.8562543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8563285Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8563852Z         
2025-03-14T04:57:21.8564258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8566101Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8567729Z         
2025-03-14T04:57:21.8568089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8568559Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:57:21.8568819Z         
2025-03-14T04:57:21.8569187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8569677Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:57:21.8569950Z         
2025-03-14T04:57:21.8570504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:21.8571207Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:57:21.8571604Z         
2025-03-14T04:57:21.8572030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:21.8572570Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:57:21.8572850Z         
2025-03-14T04:57:21.8573398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:21.8574069Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:57:21.8574350Z         
2025-03-14T04:57:21.8574751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:21.8575262Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:57:21.8575530Z         
2025-03-14T04:57:21.8576014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:21.8576669Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:57:21.8577016Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:57:21.8577299Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:57:21.8577563Z         
2025-03-14T04:57:21.8578007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:21.8578567Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:57:21.8578818Z         
2025-03-14T04:57:21.8579250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:21.8579779Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:57:21.8580030Z         
2025-03-14T04:57:21.8580524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:21.8581209Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:57:21.8581566Z         
2025-03-14T04:57:21.8582083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:21.8582681Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:57:21.8583294Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:57:21.8583901Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:57:21.8584195Z         x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:57:21.8584434Z         
2025-03-14T04:57:21.8584818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:21.8585317Z         tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32);  x_88 = None
2025-03-14T04:57:21.8585560Z         
2025-03-14T04:57:21.8585899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8586989Z         x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:57:21.8587873Z         
2025-03-14T04:57:21.8588228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:21.8588741Z         x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False);  x_89 = None
2025-03-14T04:57:21.8589041Z         
2025-03-14T04:57:21.8589502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:21.8590808Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:57:21.8591791Z         
2025-03-14T04:57:21.8592246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:21.8593481Z         x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:57:21.8594410Z         
2025-03-14T04:57:21.8594831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:57:21.8595385Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:57:21.8595720Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:57:21.8595971Z         
2025-03-14T04:57:21.8596465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:57:21.8597071Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75);  x_91 = None
2025-03-14T04:57:21.8597446Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:57:21.8597836Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:57:21.8598124Z         
2025-03-14T04:57:21.8598597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:21.8599268Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:57:21.8599576Z         
2025-03-14T04:57:21.8600078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:21.8600723Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:57:21.8601061Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:57:21.8601391Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:57:21.8601642Z         
2025-03-14T04:57:21.8602112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:21.8602707Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:57:21.8602986Z         
2025-03-14T04:57:21.8603371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:21.8603889Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:57:21.8604146Z         
2025-03-14T04:57:21.8604544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:21.8605052Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:21.8605359Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:21.8605692Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:57:21.8605957Z         
2025-03-14T04:57:21.8606354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:21.8606846Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:21.8607149Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:21.8607471Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:57:21.8607737Z         
2025-03-14T04:57:21.8608131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:21.8608615Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:21.8608877Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:57:21.8609130Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:57:21.8609365Z         
2025-03-14T04:57:21.8609755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:21.8610277Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:21.8610581Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:57:21.8610862Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:57:21.8611122Z         
2025-03-14T04:57:21.8611640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:21.8612215Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:21.8612560Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:57:21.8612806Z         
2025-03-14T04:57:21.8613218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:21.8613747Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:21.8614085Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:57:21.8614331Z         
2025-03-14T04:57:21.8614736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:21.8615267Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:21.8615609Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:57:21.8615854Z         
2025-03-14T04:57:21.8616258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:21.8616818Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:21.8617196Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:57:21.8617444Z         
2025-03-14T04:57:21.8617884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:21.8618452Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:21.8618726Z         
2025-03-14T04:57:21.8619182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:21.8619729Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:21.8619994Z         
2025-03-14T04:57:21.8620444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:21.8621006Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:21.8621343Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:57:21.8621696Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:21.8622064Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:57:21.8622338Z         
2025-03-14T04:57:21.8622794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:21.8623358Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:21.8623691Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:57:21.8624032Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:21.8624392Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:57:21.8624658Z         
2025-03-14T04:57:21.8625093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:21.8625623Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:21.8625953Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:21.8626301Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:57:21.8626551Z         
2025-03-14T04:57:21.8626962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:21.8627458Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:21.8627793Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:21.8628144Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:57:21.8628394Z         
2025-03-14T04:57:21.8628784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:21.8629238Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:21.8629497Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:21.8629728Z         
2025-03-14T04:57:21.8630122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:21.8630592Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:21.8630853Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:21.8631085Z         
2025-03-14T04:57:21.8631489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:21.8631960Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:21.8632270Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:21.8632515Z         
2025-03-14T04:57:21.8632903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:21.8633376Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:21.8633667Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:21.8633910Z         
2025-03-14T04:57:21.8634339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:21.8634932Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:21.8635243Z         
2025-03-14T04:57:21.8635682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:21.8636262Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:57:21.8636556Z         
2025-03-14T04:57:21.8637055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:21.8637706Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:57:21.8638012Z         
2025-03-14T04:57:21.8638583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:57:21.8639270Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:57:21.8639521Z         
2025-03-14T04:57:21.8639899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:21.8640388Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:57:21.8640656Z         
2025-03-14T04:57:21.8641199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:21.8641833Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:57:21.8642116Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:57:21.8642401Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:57:21.8642625Z         
2025-03-14T04:57:21.8643167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:21.8643842Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:57:21.8644307Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:57:21.8644656Z         
2025-03-14T04:57:21.8645225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:21.8645948Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:21.8646263Z         
2025-03-14T04:57:21.8646664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:21.8647191Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:57:21.8647467Z         
2025-03-14T04:57:21.8647962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:57:21.8648577Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:57:21.8648862Z         
2025-03-14T04:57:21.8649284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:21.8649833Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:57:21.8650116Z         
2025-03-14T04:57:21.8650627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:57:21.8651258Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:57:21.8651622Z         
2025-03-14T04:57:21.8652274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:57:21.8653011Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:57:21.8653337Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:21.8653709Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:57:21.8654069Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:57:21.8654334Z         
2025-03-14T04:57:21.8654812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:57:21.8655374Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:57:21.8655622Z         
2025-03-14T04:57:21.8656018Z 
2025-03-14T04:57:21.8656110Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:21.8700048Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:57:21.8744244Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:57:21.8744772Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8745572Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8746387Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8747157Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8747949Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8748719Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8749542Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8750419Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8751249Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8752008Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8752713Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8753555Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8754305Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8755017Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8755704Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8756353Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8757046Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8757808Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8758532Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8759222Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8759893Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:21.8760775Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8761550Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8762305Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:21.8763077Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:21.8763781Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8764483Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8765229Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8765951Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8766651Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8767317Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8768006Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8768744Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8769463Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8770196Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8770865Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8771612Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8772439Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8773157Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8773847Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8774496Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8775180Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8775912Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8776642Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8777333Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8778002Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8778703Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8779444Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8780165Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8780855Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8781507Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8782185Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8782916Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8783628Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8784309Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8784997Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8785687Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8786423Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8787142Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8787830Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8788498Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8789190Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8789962Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8790684Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8791429Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8792107Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8792791Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8793526Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8794240Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8794929Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8795598Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:21.8796305Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8797068Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8797800Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:21.8798520Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:21.8799199Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8799881Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8800622Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8801319Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8801987Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8802633Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8803289Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8804024Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8804740Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8805456Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8806102Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8806783Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8807510Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8808220Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8808910Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8809558Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8810270Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8811045Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8811877Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8812721Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8813455Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8814228Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8814996Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8815733Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8816481Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8817174Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8817895Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8818640Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8819371Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8820072Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8820723Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8821399Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8822132Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8822847Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8823539Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8824189Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8824872Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8825601Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8826312Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8827024Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8827671Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8828348Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8829077Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8829795Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8830485Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8831140Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8831842Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8832583Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8833331Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8834027Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8834712Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8835452Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8836195Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8836907Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8837608Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8838303Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8839030Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8839803Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8840595Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8841318Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8842034Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:21.8842786Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8843593Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8844377Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:21.8845138Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:21.8845866Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8846575Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8847393Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8848161Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8848924Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8849655Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8850415Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8851214Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8852193Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8852946Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8853624Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8854307Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8855073Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8855787Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8856488Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8857150Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8857849Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8858589Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8859308Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8860021Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8860822Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8861550Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8862322Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8863050Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8863733Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8864386Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8865069Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8865802Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8866527Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8867197Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8867832Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8868499Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8869264Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8869981Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8870658Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8871309Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8872319Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8873159Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8873534Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8873919Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8874249Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8874677Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8875075Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8875445Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8875807Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8876129Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8876511Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8876863Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8877189Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8877508Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8877796Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8878164Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8878497Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8878821Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8879127Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8879418Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8879764Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8880107Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8880446Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8880759Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8881068Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:57:21.8881425Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8881768Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8882089Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.8882406Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.8882689Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:57:21.8883039Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8883380Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8883695Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.8884009Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.8884297Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:57:21.8884682Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.8885036Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.8885382Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.8885716Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.8886086Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:57:21.8886427Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:57:21.8886752Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:57:21.8887165Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:57:21.8887539Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:57:21.8887951Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:57:21.8888313Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:57:21.8888389Z         
2025-03-14T04:57:21.8888688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8889208Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8889283Z         
2025-03-14T04:57:21.8889579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8891153Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8891245Z         
2025-03-14T04:57:21.8891633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:57:21.8891798Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:57:21.8891879Z         
2025-03-14T04:57:21.8892289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:57:21.8892559Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:57:21.8892629Z         
2025-03-14T04:57:21.8892893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8893341Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8893403Z         
2025-03-14T04:57:21.8893676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8895259Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8895352Z         
2025-03-14T04:57:21.8895646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8895785Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:57:21.8895854Z         
2025-03-14T04:57:21.8896103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8896541Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8896603Z         
2025-03-14T04:57:21.8896873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8898423Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8898510Z         
2025-03-14T04:57:21.8898810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8898951Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:57:21.8899021Z         
2025-03-14T04:57:21.8899275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8899726Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8899793Z         
2025-03-14T04:57:21.8900069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8901659Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8901744Z         
2025-03-14T04:57:21.8901997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8902441Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:21.8902508Z         
2025-03-14T04:57:21.8902771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8904386Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8904457Z         
2025-03-14T04:57:21.8904742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8904897Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:57:21.8904974Z         
2025-03-14T04:57:21.8905267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8905416Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:57:21.8905482Z         
2025-03-14T04:57:21.8905731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8906163Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8906226Z         
2025-03-14T04:57:21.8906496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8908046Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8908130Z         
2025-03-14T04:57:21.8908437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8908577Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:57:21.8908648Z         
2025-03-14T04:57:21.8908904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8909345Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8909407Z         
2025-03-14T04:57:21.8909686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8911261Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8911332Z         
2025-03-14T04:57:21.8911638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8911786Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:57:21.8911853Z         
2025-03-14T04:57:21.8912104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8912543Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8912603Z         
2025-03-14T04:57:21.8912874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8914445Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8914524Z         
2025-03-14T04:57:21.8914812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8914984Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:57:21.8915053Z         
2025-03-14T04:57:21.8915335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8915485Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:57:21.8915545Z         
2025-03-14T04:57:21.8915807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8916234Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8916307Z         
2025-03-14T04:57:21.8916576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8918140Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8918229Z         
2025-03-14T04:57:21.8918514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8918656Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:57:21.8918716Z         
2025-03-14T04:57:21.8918980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8919405Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8919476Z         
2025-03-14T04:57:21.8919742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8921276Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8921362Z         
2025-03-14T04:57:21.8921654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8921796Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:57:21.8921853Z         
2025-03-14T04:57:21.8922101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8922530Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8922601Z         
2025-03-14T04:57:21.8922861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8924440Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8924508Z         
2025-03-14T04:57:21.8924793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8924953Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:57:21.8925011Z         
2025-03-14T04:57:21.8925288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8925438Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:57:21.8925503Z         
2025-03-14T04:57:21.8925751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8926185Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8926246Z         
2025-03-14T04:57:21.8926513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8928089Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8928170Z         
2025-03-14T04:57:21.8928460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8928603Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:57:21.8928672Z         
2025-03-14T04:57:21.8928921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8929361Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8929429Z         
2025-03-14T04:57:21.8929694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8931273Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8931363Z         
2025-03-14T04:57:21.8931767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8931934Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:57:21.8932010Z         
2025-03-14T04:57:21.8932300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8932752Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8932823Z         
2025-03-14T04:57:21.8933094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8934626Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8934707Z         
2025-03-14T04:57:21.8934978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8935424Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:21.8935485Z         
2025-03-14T04:57:21.8935752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8937327Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8937396Z         
2025-03-14T04:57:21.8937669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8937821Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:57:21.8937892Z         
2025-03-14T04:57:21.8938173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8938325Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:57:21.8938383Z         
2025-03-14T04:57:21.8938628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8939038Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8939104Z         
2025-03-14T04:57:21.8939358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8940908Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8940989Z         
2025-03-14T04:57:21.8941277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8941421Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:57:21.8941480Z         
2025-03-14T04:57:21.8941726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8942141Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8942207Z         
2025-03-14T04:57:21.8942461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8943960Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8944027Z         
2025-03-14T04:57:21.8944300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8944465Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:57:21.8944523Z         
2025-03-14T04:57:21.8944770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8945186Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8945251Z         
2025-03-14T04:57:21.8945506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8947018Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8947102Z         
2025-03-14T04:57:21.8947376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8947549Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:57:21.8947611Z         
2025-03-14T04:57:21.8947900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8948048Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:57:21.8948117Z         
2025-03-14T04:57:21.8948365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8948792Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8948855Z         
2025-03-14T04:57:21.8949122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8950676Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8950763Z         
2025-03-14T04:57:21.8951053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8951192Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:57:21.8951257Z         
2025-03-14T04:57:21.8951501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8951950Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8952012Z         
2025-03-14T04:57:21.8952287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8953861Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8953941Z         
2025-03-14T04:57:21.8954244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8954383Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:57:21.8954450Z         
2025-03-14T04:57:21.8954696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8955136Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8955197Z         
2025-03-14T04:57:21.8955465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8957008Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8957071Z         
2025-03-14T04:57:21.8957379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8957528Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:57:21.8957596Z         
2025-03-14T04:57:21.8957878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8958032Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:57:21.8958092Z         
2025-03-14T04:57:21.8958348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8958773Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8958844Z         
2025-03-14T04:57:21.8959108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8960854Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8960952Z         
2025-03-14T04:57:21.8961238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8961384Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:57:21.8961445Z         
2025-03-14T04:57:21.8961701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8962130Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8962203Z         
2025-03-14T04:57:21.8962464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8964032Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8964127Z         
2025-03-14T04:57:21.8964409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8964557Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:57:21.8964619Z         
2025-03-14T04:57:21.8964877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8965312Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8965384Z         
2025-03-14T04:57:21.8965652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8967222Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8967308Z         
2025-03-14T04:57:21.8967611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8967773Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:57:21.8967834Z         
2025-03-14T04:57:21.8968128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8968277Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:57:21.8968345Z         
2025-03-14T04:57:21.8968596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8969028Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8969097Z         
2025-03-14T04:57:21.8969362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8970912Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8970991Z         
2025-03-14T04:57:21.8971283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8971461Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:57:21.8971539Z         
2025-03-14T04:57:21.8971790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8972219Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8972288Z         
2025-03-14T04:57:21.8972549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8974098Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8974177Z         
2025-03-14T04:57:21.8974466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8974600Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:57:21.8974669Z         
2025-03-14T04:57:21.8974918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8975351Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8975420Z         
2025-03-14T04:57:21.8975684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8977243Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8977322Z         
2025-03-14T04:57:21.8977616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8978059Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:21.8978119Z         
2025-03-14T04:57:21.8978388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8979995Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8980065Z         
2025-03-14T04:57:21.8980359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8980503Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:57:21.8980583Z         
2025-03-14T04:57:21.8980861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8981006Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:57:21.8981064Z         
2025-03-14T04:57:21.8981327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8981737Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8981805Z         
2025-03-14T04:57:21.8982064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8983622Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8983708Z         
2025-03-14T04:57:21.8983989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8984125Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:57:21.8984184Z         
2025-03-14T04:57:21.8984440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8984859Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8984929Z         
2025-03-14T04:57:21.8985191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8986752Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8986841Z         
2025-03-14T04:57:21.8987134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8987272Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:57:21.8987333Z         
2025-03-14T04:57:21.8987586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8988005Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8988073Z         
2025-03-14T04:57:21.8988332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8989876Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8989944Z         
2025-03-14T04:57:21.8990211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.8990381Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:57:21.8990441Z         
2025-03-14T04:57:21.8990725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8990861Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:57:21.8990929Z         
2025-03-14T04:57:21.8991170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8991574Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.8991635Z         
2025-03-14T04:57:21.8991898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8993424Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8993534Z         
2025-03-14T04:57:21.8993826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8993955Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:57:21.8994024Z         
2025-03-14T04:57:21.8994273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8994701Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.8994760Z         
2025-03-14T04:57:21.8995024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8996544Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8996627Z         
2025-03-14T04:57:21.8996928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.8997054Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:57:21.8997118Z         
2025-03-14T04:57:21.8997362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.8997782Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.8997843Z         
2025-03-14T04:57:21.8998111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.8999678Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.8999765Z         
2025-03-14T04:57:21.9000070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9000212Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:57:21.9000278Z         
2025-03-14T04:57:21.9000551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9000696Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:57:21.9000756Z         
2025-03-14T04:57:21.9001006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9001414Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9001482Z         
2025-03-14T04:57:21.9001740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9003236Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9003321Z         
2025-03-14T04:57:21.9003600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9003735Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:57:21.9003795Z         
2025-03-14T04:57:21.9004050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9004477Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9004547Z         
2025-03-14T04:57:21.9004808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9006378Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9006484Z         
2025-03-14T04:57:21.9006764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9006898Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:57:21.9006958Z         
2025-03-14T04:57:21.9007213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9007631Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9007701Z         
2025-03-14T04:57:21.9007959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9009492Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9009576Z         
2025-03-14T04:57:21.9009855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9010006Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:57:21.9010066Z         
2025-03-14T04:57:21.9010358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9010505Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:57:21.9010575Z         
2025-03-14T04:57:21.9010836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9011279Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9011343Z         
2025-03-14T04:57:21.9011705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9013412Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9013494Z         
2025-03-14T04:57:21.9013798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9013945Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:57:21.9014015Z         
2025-03-14T04:57:21.9014260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9014687Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9014751Z         
2025-03-14T04:57:21.9015018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9016566Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9016652Z         
2025-03-14T04:57:21.9016959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9017103Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:57:21.9017171Z         
2025-03-14T04:57:21.9017413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9017841Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9017911Z         
2025-03-14T04:57:21.9018173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9019735Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9019825Z         
2025-03-14T04:57:21.9020113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9020255Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:57:21.9020323Z         
2025-03-14T04:57:21.9020600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9020748Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:57:21.9020810Z         
2025-03-14T04:57:21.9021064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9021479Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9021548Z         
2025-03-14T04:57:21.9021815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9023348Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9023437Z         
2025-03-14T04:57:21.9023721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9023861Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:57:21.9023920Z         
2025-03-14T04:57:21.9024176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9024626Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9024692Z         
2025-03-14T04:57:21.9024980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9026532Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9026620Z         
2025-03-14T04:57:21.9026906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9027043Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:57:21.9027111Z         
2025-03-14T04:57:21.9027360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9027785Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9027848Z         
2025-03-14T04:57:21.9028115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9029647Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9029734Z         
2025-03-14T04:57:21.9030017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9030158Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:57:21.9030229Z         
2025-03-14T04:57:21.9030507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9030652Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:57:21.9030713Z         
2025-03-14T04:57:21.9031169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:21.9031317Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:57:21.9031384Z         
2025-03-14T04:57:21.9031676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:21.9031816Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:57:21.9031895Z         
2025-03-14T04:57:21.9032340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:21.9032504Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:57:21.9032573Z         
2025-03-14T04:57:21.9032877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:21.9033022Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:57:21.9033081Z         
2025-03-14T04:57:21.9033465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:21.9033646Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:57:21.9033748Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:57:21.9033865Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:57:21.9033934Z         
2025-03-14T04:57:21.9034265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:21.9034394Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:57:21.9034454Z         
2025-03-14T04:57:21.9034788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:21.9034904Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:57:21.9034972Z         
2025-03-14T04:57:21.9035352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:21.9035573Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:57:21.9035651Z         
2025-03-14T04:57:21.9036077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:21.9036196Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:57:21.9036628Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:57:21.9036751Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:57:21.9036858Z         x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:57:21.9036924Z         
2025-03-14T04:57:21.9037213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:21.9037344Z         tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32);  x_88 = None
2025-03-14T04:57:21.9037403Z         
2025-03-14T04:57:21.9037656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9038425Z         x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:57:21.9038505Z         
2025-03-14T04:57:21.9038773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:21.9038973Z         x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False);  x_89 = None
2025-03-14T04:57:21.9039033Z         
2025-03-14T04:57:21.9039411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:21.9040246Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:57:21.9040315Z         
2025-03-14T04:57:21.9040672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:21.9041462Z         x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:57:21.9041528Z         
2025-03-14T04:57:21.9041855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:57:21.9042061Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:57:21.9042197Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:57:21.9042264Z         
2025-03-14T04:57:21.9042667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:57:21.9042825Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75);  x_91 = None
2025-03-14T04:57:21.9042988Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:57:21.9043162Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:57:21.9043223Z         
2025-03-14T04:57:21.9043624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:21.9043821Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:57:21.9043886Z         
2025-03-14T04:57:21.9044328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:21.9044469Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:57:21.9044618Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:57:21.9044767Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:57:21.9044836Z         
2025-03-14T04:57:21.9045207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:21.9045383Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:57:21.9045443Z         
2025-03-14T04:57:21.9045760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:21.9045900Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:57:21.9045967Z         
2025-03-14T04:57:21.9046276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:21.9046409Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:21.9046533Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:21.9046682Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:57:21.9046743Z         
2025-03-14T04:57:21.9047065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:21.9047183Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:21.9047306Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:21.9047447Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:57:21.9047514Z         
2025-03-14T04:57:21.9047819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:21.9047961Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:21.9048044Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:57:21.9048170Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:57:21.9048231Z         
2025-03-14T04:57:21.9048543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:21.9048685Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:21.9048777Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:57:21.9048896Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:57:21.9048964Z         
2025-03-14T04:57:21.9049301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:21.9049464Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:21.9049579Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:57:21.9049651Z         
2025-03-14T04:57:21.9049964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:21.9050144Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:21.9050257Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:57:21.9050328Z         
2025-03-14T04:57:21.9050639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:21.9050830Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:21.9050951Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:57:21.9051018Z         
2025-03-14T04:57:21.9051327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:21.9051608Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:21.9051740Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:57:21.9051808Z         
2025-03-14T04:57:21.9052184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:21.9052339Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:21.9052417Z         
2025-03-14T04:57:21.9052766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:21.9052906Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:21.9052966Z         
2025-03-14T04:57:21.9053316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:21.9053462Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:21.9053585Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:57:21.9053735Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:21.9053897Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:57:21.9053958Z         
2025-03-14T04:57:21.9054305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:21.9054439Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:21.9054563Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:57:21.9054711Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:21.9054849Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:57:21.9054912Z         
2025-03-14T04:57:21.9055253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:21.9055370Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:21.9055539Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:21.9055665Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:57:21.9055732Z         
2025-03-14T04:57:21.9056309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:21.9056433Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:21.9056597Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:21.9056753Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:57:21.9056816Z         
2025-03-14T04:57:21.9057147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:21.9057242Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:21.9057369Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:21.9057430Z         
2025-03-14T04:57:21.9057744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:21.9057834Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:21.9057953Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:21.9058015Z         
2025-03-14T04:57:21.9058334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:21.9058444Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:21.9058571Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:21.9058630Z         
2025-03-14T04:57:21.9058928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:21.9059033Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:21.9059161Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:21.9059222Z         
2025-03-14T04:57:21.9059573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:21.9059773Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:21.9059849Z         
2025-03-14T04:57:21.9060185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:21.9060340Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:57:21.9060407Z         
2025-03-14T04:57:21.9060925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:21.9061102Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:57:21.9061163Z         
2025-03-14T04:57:21.9061643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:57:21.9061772Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:57:21.9061840Z         
2025-03-14T04:57:21.9062128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:21.9062268Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:57:21.9062327Z         
2025-03-14T04:57:21.9062792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:21.9062932Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:57:21.9063037Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:57:21.9063177Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:57:21.9063245Z         
2025-03-14T04:57:21.9063696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:21.9063858Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:57:21.9064087Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:57:21.9064158Z         
2025-03-14T04:57:21.9064603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:21.9064773Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:21.9064831Z         
2025-03-14T04:57:21.9065126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:21.9065268Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:57:21.9065335Z         
2025-03-14T04:57:21.9065707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:57:21.9065849Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:57:21.9065907Z         
2025-03-14T04:57:21.9066202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:21.9066362Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:57:21.9066429Z         
2025-03-14T04:57:21.9066798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:57:21.9066937Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:57:21.9067004Z         
2025-03-14T04:57:21.9067486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:57:21.9067627Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:57:21.9067745Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:21.9067903Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:57:21.9068040Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:57:21.9068106Z         
2025-03-14T04:57:21.9068462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:57:21.9068596Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:57:21.9068656Z         
2025-03-14T04:57:21.9068663Z 
2025-03-14T04:57:21.9068758Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:21.9110806Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:57:21.9111200Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:57:21.9111549Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:57:21.9111974Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9112368Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9112747Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.9113113Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.9113493Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:21.9113905Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9114308Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9114723Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.9115106Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.9115436Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:21.9115855Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9116281Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9116668Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.9117044Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.9117356Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:21.9117778Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9118177Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9118564Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.9118953Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.9119345Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:21.9119795Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9120200Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9120619Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:21.9121007Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:21.9121369Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:21.9121768Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9122167Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9122555Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.9122921Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.9123246Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:21.9123674Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9124075Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9124462Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.9124836Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.9125158Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:21.9125545Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9125924Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9126290Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.9126646Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.9126990Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:21.9127371Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9127733Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9128097Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.9128452Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.9128777Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:21.9129171Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9129546Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9129920Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.9130279Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.9130624Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:21.9131015Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9131450Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9131881Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.9132281Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.9132651Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:21.9133064Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9133464Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9133823Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.9134186Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.9134511Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:21.9134897Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9135282Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9135630Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.9135973Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.9136280Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:21.9136653Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9137026Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9137376Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.9137710Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.9138063Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:21.9138452Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9138840Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9139208Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:21.9139559Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:21.9139841Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:21.9140168Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9140520Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9140831Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.9141176Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.9141460Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:21.9141790Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9142120Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9142429Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.9142738Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.9143013Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:21.9143353Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9143676Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9143996Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.9144329Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.9144604Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:21.9144943Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9145265Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9145582Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.9145883Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.9146163Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:21.9146507Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9146855Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9147181Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.9147537Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.9147823Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:21.9148152Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9148486Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9148798Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.9149109Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.9149413Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:57:21.9149793Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9150162Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9150531Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.9150876Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.9151189Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:57:21.9151568Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9151931Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9152280Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.9152582Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.9152869Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:57:21.9153215Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9153604Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9153978Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.9154315Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.9154628Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:21.9155000Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9155353Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9155666Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.9155974Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.9156268Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:21.9156641Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9157009Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9157373Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.9157716Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.9158021Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:21.9158397Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9158762Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9159116Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.9159451Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.9159796Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:21.9160187Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9160747Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9161129Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:21.9161480Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:21.9161800Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:21.9162169Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9162546Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9162881Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.9163197Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.9163499Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:21.9163844Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9164229Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9164555Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.9164881Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.9165170Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:21.9165527Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9165884Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9166218Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.9166572Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.9166875Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:21.9167282Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9167675Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9168023Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.9168361Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.9168668Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:21.9169046Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9169410Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9169755Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.9170095Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.9170420Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:21.9170807Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9171219Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9171641Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.9172021Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.9172368Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:57:21.9172757Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9173161Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9173530Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.9173906Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.9174237Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:57:21.9174670Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9175057Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9175429Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.9175790Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.9176107Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:57:21.9176506Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9176878Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9177243Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.9177589Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.9177914Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:57:21.9178315Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9178698Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9179062Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.9179408Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.9179723Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:57:21.9180090Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9180459Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9180825Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.9181178Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.9181501Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:57:21.9181899Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9182286Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9182600Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.9182914Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.9183192Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:57:21.9183542Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9183873Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9184196Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:21.9184501Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:21.9184792Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:57:21.9185153Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9185490Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9185816Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:21.9186128Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:21.9186426Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:57:21.9186766Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:21.9187113Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:21.9187445Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:21.9187764Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:21.9188147Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:57:21.9188464Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:57:21.9188777Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:57:21.9189145Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:57:21.9189507Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:57:21.9189862Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:57:21.9190203Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:57:21.9190268Z         
2025-03-14T04:57:21.9190558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9191028Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9191116Z         
2025-03-14T04:57:21.9191396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9192847Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9192920Z         
2025-03-14T04:57:21.9193199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:57:21.9193350Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:57:21.9193412Z         
2025-03-14T04:57:21.9193788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:57:21.9194055Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:57:21.9194127Z         
2025-03-14T04:57:21.9194424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9194899Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9194972Z         
2025-03-14T04:57:21.9195243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9196828Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9196893Z         
2025-03-14T04:57:21.9197196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9197335Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:57:21.9197402Z         
2025-03-14T04:57:21.9197661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9198096Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9198180Z         
2025-03-14T04:57:21.9198444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9199985Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9200049Z         
2025-03-14T04:57:21.9200336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9200480Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:57:21.9200573Z         
2025-03-14T04:57:21.9200827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9201260Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9201357Z         
2025-03-14T04:57:21.9201619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9203165Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9203238Z         
2025-03-14T04:57:21.9203489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9203931Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:21.9203996Z         
2025-03-14T04:57:21.9204266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9205867Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9205953Z         
2025-03-14T04:57:21.9206242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9206398Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:57:21.9206468Z         
2025-03-14T04:57:21.9206767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9206928Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:57:21.9206991Z         
2025-03-14T04:57:21.9207278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9207733Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9207824Z         
2025-03-14T04:57:21.9208129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9209772Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9209845Z         
2025-03-14T04:57:21.9210140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9210293Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:57:21.9210358Z         
2025-03-14T04:57:21.9210626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9211102Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9211181Z         
2025-03-14T04:57:21.9211581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9213308Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9213383Z         
2025-03-14T04:57:21.9213687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9213835Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:57:21.9213897Z         
2025-03-14T04:57:21.9214159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9214643Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9214734Z         
2025-03-14T04:57:21.9215014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9216668Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9216741Z         
2025-03-14T04:57:21.9217032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9217203Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:57:21.9217266Z         
2025-03-14T04:57:21.9217569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9217718Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:57:21.9217786Z         
2025-03-14T04:57:21.9218048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9218494Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9218581Z         
2025-03-14T04:57:21.9218871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9220524Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9220597Z         
2025-03-14T04:57:21.9220908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9221053Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:57:21.9221126Z         
2025-03-14T04:57:21.9221425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9221882Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9221965Z         
2025-03-14T04:57:21.9222272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9223909Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9223976Z         
2025-03-14T04:57:21.9224288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9224442Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:57:21.9224510Z         
2025-03-14T04:57:21.9224762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9225199Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9225262Z         
2025-03-14T04:57:21.9225538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9227112Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9227175Z         
2025-03-14T04:57:21.9227460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9227611Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:57:21.9227679Z         
2025-03-14T04:57:21.9227959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9228136Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:57:21.9228196Z         
2025-03-14T04:57:21.9228452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9228904Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9228973Z         
2025-03-14T04:57:21.9229234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9230817Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9230886Z         
2025-03-14T04:57:21.9231172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9231321Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:57:21.9231382Z         
2025-03-14T04:57:21.9231639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9232070Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9232155Z         
2025-03-14T04:57:21.9232419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9233967Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9234038Z         
2025-03-14T04:57:21.9234318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9234465Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:57:21.9234525Z         
2025-03-14T04:57:21.9234798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9235233Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9235317Z         
2025-03-14T04:57:21.9235618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9237160Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9237232Z         
2025-03-14T04:57:21.9237477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9237928Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:21.9237995Z         
2025-03-14T04:57:21.9238254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9239862Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9239941Z         
2025-03-14T04:57:21.9240226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9240374Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:57:21.9240447Z         
2025-03-14T04:57:21.9240727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9240886Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:57:21.9240946Z         
2025-03-14T04:57:21.9241215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9241643Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9241720Z         
2025-03-14T04:57:21.9242008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9243559Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9243629Z         
2025-03-14T04:57:21.9243915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9244061Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:57:21.9244128Z         
2025-03-14T04:57:21.9244368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9244793Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9244854Z         
2025-03-14T04:57:21.9245114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9246653Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9246719Z         
2025-03-14T04:57:21.9247012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9247149Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:57:21.9247213Z         
2025-03-14T04:57:21.9247459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9247917Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9247976Z         
2025-03-14T04:57:21.9248276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9249917Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9249989Z         
2025-03-14T04:57:21.9250286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9250444Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:57:21.9250515Z         
2025-03-14T04:57:21.9250820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9250991Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:57:21.9251058Z         
2025-03-14T04:57:21.9251346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9251878Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9251980Z         
2025-03-14T04:57:21.9252284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9253906Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9253977Z         
2025-03-14T04:57:21.9254258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9254408Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:57:21.9254470Z         
2025-03-14T04:57:21.9254742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9255175Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9255297Z         
2025-03-14T04:57:21.9255574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9257118Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9257189Z         
2025-03-14T04:57:21.9257471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9257614Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:57:21.9257675Z         
2025-03-14T04:57:21.9257925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9258349Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9258418Z         
2025-03-14T04:57:21.9258678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9260231Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9260303Z         
2025-03-14T04:57:21.9260742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9260922Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:57:21.9260985Z         
2025-03-14T04:57:21.9261280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9261468Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:57:21.9261535Z         
2025-03-14T04:57:21.9261783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9262253Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9262314Z         
2025-03-14T04:57:21.9262584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9264123Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9264187Z         
2025-03-14T04:57:21.9264478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9264616Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:57:21.9264685Z         
2025-03-14T04:57:21.9264930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9265367Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9265453Z         
2025-03-14T04:57:21.9265722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9267267Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9267329Z         
2025-03-14T04:57:21.9267619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9267755Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:57:21.9267823Z         
2025-03-14T04:57:21.9268084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9268520Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9268598Z         
2025-03-14T04:57:21.9268890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9270436Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9270500Z         
2025-03-14T04:57:21.9270781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9270929Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:57:21.9271000Z         
2025-03-14T04:57:21.9271280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9271430Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:57:21.9271490Z         
2025-03-14T04:57:21.9271747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9272180Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9272250Z         
2025-03-14T04:57:21.9272509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9274034Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9274104Z         
2025-03-14T04:57:21.9274382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9274539Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:57:21.9274601Z         
2025-03-14T04:57:21.9274852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9275316Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9275389Z         
2025-03-14T04:57:21.9275663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9277261Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9277336Z         
2025-03-14T04:57:21.9277628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9277774Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:57:21.9277837Z         
2025-03-14T04:57:21.9278102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9278556Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9278643Z         
2025-03-14T04:57:21.9278928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9280545Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9280623Z         
2025-03-14T04:57:21.9280881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9281360Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:21.9281435Z         
2025-03-14T04:57:21.9281713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9283440Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9283508Z         
2025-03-14T04:57:21.9283813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9283960Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:57:21.9284035Z         
2025-03-14T04:57:21.9284334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9284490Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:57:21.9284554Z         
2025-03-14T04:57:21.9284820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9285263Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9285352Z         
2025-03-14T04:57:21.9285636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9287266Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9287346Z         
2025-03-14T04:57:21.9287659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9287812Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:57:21.9287885Z         
2025-03-14T04:57:21.9288164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9288661Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9288743Z         
2025-03-14T04:57:21.9289051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9290815Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9290894Z         
2025-03-14T04:57:21.9291215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9291357Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:57:21.9291477Z         
2025-03-14T04:57:21.9291761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9292242Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9292308Z         
2025-03-14T04:57:21.9292606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9294441Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9294526Z         
2025-03-14T04:57:21.9294840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9295000Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:57:21.9295071Z         
2025-03-14T04:57:21.9295374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9295533Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:57:21.9295597Z         
2025-03-14T04:57:21.9295893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9296351Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9296453Z         
2025-03-14T04:57:21.9296753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9298416Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9298496Z         
2025-03-14T04:57:21.9298806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9298955Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:57:21.9299021Z         
2025-03-14T04:57:21.9299302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9299750Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9299842Z         
2025-03-14T04:57:21.9300129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9301814Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9301889Z         
2025-03-14T04:57:21.9302194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9302339Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:57:21.9302404Z         
2025-03-14T04:57:21.9302676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9303157Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9303242Z         
2025-03-14T04:57:21.9303504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9305161Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9305236Z         
2025-03-14T04:57:21.9305531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9305690Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:57:21.9305754Z         
2025-03-14T04:57:21.9306057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9306205Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:57:21.9306274Z         
2025-03-14T04:57:21.9306534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9306980Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9307058Z         
2025-03-14T04:57:21.9307344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9308919Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9308982Z         
2025-03-14T04:57:21.9309287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9309423Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:57:21.9309494Z         
2025-03-14T04:57:21.9309769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9310220Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9310303Z         
2025-03-14T04:57:21.9310602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9312242Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9312311Z         
2025-03-14T04:57:21.9312613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9312750Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:57:21.9312820Z         
2025-03-14T04:57:21.9313084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9313534Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9313614Z         
2025-03-14T04:57:21.9313900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9315511Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9315581Z         
2025-03-14T04:57:21.9315900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9316054Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:57:21.9316137Z         
2025-03-14T04:57:21.9316430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9316598Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:57:21.9316661Z         
2025-03-14T04:57:21.9316928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9317400Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9317473Z         
2025-03-14T04:57:21.9317749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9319328Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9319400Z         
2025-03-14T04:57:21.9319682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9319817Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:57:21.9319878Z         
2025-03-14T04:57:21.9320131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9320556Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9320829Z         
2025-03-14T04:57:21.9321108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9322760Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9322836Z         
2025-03-14T04:57:21.9323134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9323281Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:57:21.9323344Z         
2025-03-14T04:57:21.9323643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9324089Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9324182Z         
2025-03-14T04:57:21.9324471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9326102Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9326179Z         
2025-03-14T04:57:21.9326468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9326626Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:57:21.9326689Z         
2025-03-14T04:57:21.9326994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9327139Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:57:21.9327211Z         
2025-03-14T04:57:21.9327469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9327928Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:21.9328000Z         
2025-03-14T04:57:21.9328275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9329925Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9329991Z         
2025-03-14T04:57:21.9330298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9330454Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:57:21.9330527Z         
2025-03-14T04:57:21.9330789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9331292Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:21.9331367Z         
2025-03-14T04:57:21.9331720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9333385Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9333450Z         
2025-03-14T04:57:21.9333755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9333890Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:57:21.9333962Z         
2025-03-14T04:57:21.9334220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9334673Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:21.9334763Z         
2025-03-14T04:57:21.9335043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:21.9336656Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:21.9336725Z         
2025-03-14T04:57:21.9337026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:21.9337179Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:57:21.9337251Z         
2025-03-14T04:57:21.9337564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:21.9337720Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:57:21.9337810Z         
2025-03-14T04:57:21.9338289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:21.9338456Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:57:21.9338521Z         
2025-03-14T04:57:21.9338839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:21.9338981Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:57:21.9339051Z         
2025-03-14T04:57:21.9339511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:21.9339674Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:57:21.9339738Z         
2025-03-14T04:57:21.9340050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:21.9340184Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:57:21.9340253Z         
2025-03-14T04:57:21.9340628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:21.9340814Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:57:21.9340909Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:57:21.9341037Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:57:21.9341097Z         
2025-03-14T04:57:21.9341451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:21.9341574Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:57:21.9341643Z         
2025-03-14T04:57:21.9341984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:21.9342109Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:57:21.9342171Z         
2025-03-14T04:57:21.9342555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:21.9342767Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:57:21.9342837Z         
2025-03-14T04:57:21.9343249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:21.9343377Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:57:21.9343817Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:57:21.9343946Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:57:21.9344072Z         x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:57:21.9344141Z         
2025-03-14T04:57:21.9344453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:21.9344584Z         tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32);  x_88 = None
2025-03-14T04:57:21.9344654Z         
2025-03-14T04:57:21.9344902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:21.9345672Z         x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:57:21.9345734Z         
2025-03-14T04:57:21.9346014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:21.9346197Z         x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False);  x_89 = None
2025-03-14T04:57:21.9346267Z         
2025-03-14T04:57:21.9346644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:21.9347503Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:57:21.9347590Z         
2025-03-14T04:57:21.9347958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:21.9348782Z         x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:57:21.9348846Z         
2025-03-14T04:57:21.9349203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:57:21.9349360Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:57:21.9349509Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:57:21.9349573Z         
2025-03-14T04:57:21.9350006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:57:21.9350180Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75);  x_91 = None
2025-03-14T04:57:21.9350365Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:57:21.9350560Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:57:21.9350635Z         
2025-03-14T04:57:21.9351058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:21.9351273Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:57:21.9351338Z         
2025-03-14T04:57:21.9351796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:21.9351944Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:57:21.9352097Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:57:21.9352245Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:57:21.9352309Z         
2025-03-14T04:57:21.9352709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:21.9352880Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:57:21.9352948Z         
2025-03-14T04:57:21.9353277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:21.9353427Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:57:21.9353490Z         
2025-03-14T04:57:21.9353821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:21.9353972Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:21.9354107Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:21.9354251Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:57:21.9354319Z         
2025-03-14T04:57:21.9354638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:21.9354770Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:21.9354889Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:21.9355041Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:57:21.9355104Z         
2025-03-14T04:57:21.9355424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:21.9355547Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:21.9355655Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:57:21.9355774Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:57:21.9355842Z         
2025-03-14T04:57:21.9356153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:21.9356317Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:21.9356405Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:57:21.9356554Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:57:21.9356616Z         
2025-03-14T04:57:21.9356967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:21.9357121Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:21.9357242Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:57:21.9357301Z         
2025-03-14T04:57:21.9357606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:21.9357760Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:21.9357877Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:57:21.9357939Z         
2025-03-14T04:57:21.9358244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:21.9358395Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:21.9358514Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:57:21.9358574Z         
2025-03-14T04:57:21.9358884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:21.9359072Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:21.9359179Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:57:21.9359247Z         
2025-03-14T04:57:21.9359579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:21.9359722Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:21.9359799Z         
2025-03-14T04:57:21.9360135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:21.9360267Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:21.9360334Z         
2025-03-14T04:57:21.9360839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:21.9360989Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:21.9361110Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:57:21.9361269Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:21.9361405Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:57:21.9361477Z         
2025-03-14T04:57:21.9361819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:21.9361962Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:21.9362147Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:57:21.9362305Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:21.9362437Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:57:21.9362527Z         
2025-03-14T04:57:21.9362851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:21.9362996Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:21.9363164Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:21.9363294Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:57:21.9363354Z         
2025-03-14T04:57:21.9363693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:21.9363805Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:21.9363976Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:21.9364112Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:57:21.9364184Z         
2025-03-14T04:57:21.9364500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:21.9364603Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:21.9364718Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:21.9364786Z         
2025-03-14T04:57:21.9365097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:21.9365195Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:21.9365306Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:21.9365376Z         
2025-03-14T04:57:21.9365682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:21.9365825Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:21.9365948Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:21.9366018Z         
2025-03-14T04:57:21.9366316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:21.9366433Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:21.9366553Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:21.9366624Z         
2025-03-14T04:57:21.9366967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:21.9367153Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:21.9367214Z         
2025-03-14T04:57:21.9367553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:21.9367721Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:57:21.9367782Z         
2025-03-14T04:57:21.9368193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:21.9368367Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:57:21.9368453Z         
2025-03-14T04:57:21.9368971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:57:21.9369118Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:57:21.9369183Z         
2025-03-14T04:57:21.9369503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:21.9369651Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:57:21.9369727Z         
2025-03-14T04:57:21.9370187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:21.9370318Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:57:21.9370433Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:57:21.9370564Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:57:21.9370631Z         
2025-03-14T04:57:21.9371128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:21.9371305Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:57:21.9371615Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:57:21.9371685Z         
2025-03-14T04:57:21.9372176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:21.9372370Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:21.9372442Z         
2025-03-14T04:57:21.9372749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:21.9372912Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:57:21.9372977Z         
2025-03-14T04:57:21.9373387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:57:21.9373537Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:57:21.9373611Z         
2025-03-14T04:57:21.9373920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:21.9374080Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:57:21.9374143Z         
2025-03-14T04:57:21.9374543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:57:21.9374683Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:57:21.9374771Z         
2025-03-14T04:57:21.9375288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:57:21.9375448Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:57:21.9375597Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:21.9375757Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:57:21.9375900Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:57:21.9375965Z         
2025-03-14T04:57:21.9376367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:57:21.9376489Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:57:21.9376563Z         
2025-03-14T04:57:29.4015503Z 
2025-03-14T04:57:29.4016379Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:29.4018123Z     def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:57:29.4019778Z         l_features_res4_ = L_features_res4_
2025-03-14T04:57:29.4020354Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:57:29.4021017Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:57:29.4021928Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:57:29.4022591Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:57:29.4023327Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:57:29.4024083Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:57:29.4024764Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:57:29.4025229Z         
2025-03-14T04:57:29.4025887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:29.4026622Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:57:29.4026913Z         
2025-03-14T04:57:29.4027339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:29.4027887Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:57:29.4028146Z         
2025-03-14T04:57:29.4028810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:29.4029491Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:57:29.4029758Z         
2025-03-14T04:57:29.4030187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:29.4030658Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:57:29.4030912Z         
2025-03-14T04:57:29.4031370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:29.4031969Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:57:29.4032299Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:57:29.4032561Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:57:29.4032797Z         
2025-03-14T04:57:29.4033210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:29.4033718Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:57:29.4033958Z         
2025-03-14T04:57:29.4034362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:29.4034856Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:57:29.4035090Z         
2025-03-14T04:57:29.4035628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:29.4036265Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:57:29.4036593Z         
2025-03-14T04:57:29.4037092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:29.4037717Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:57:29.4038198Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:57:29.4038680Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:57:29.4038963Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:57:29.4039192Z         
2025-03-14T04:57:29.4039573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:29.4040043Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:57:29.4040281Z         
2025-03-14T04:57:29.4040621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:29.4041558Z         x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:57:29.4042282Z         
2025-03-14T04:57:29.4042638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:29.4043206Z         x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:57:29.4043497Z         
2025-03-14T04:57:29.4043965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:29.4045039Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:57:29.4045788Z         
2025-03-14T04:57:29.4046231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:29.4047243Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:57:29.4047949Z         
2025-03-14T04:57:29.4048369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:57:29.4048911Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:57:29.4049256Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:57:29.4049516Z         
2025-03-14T04:57:29.4050011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:57:29.4050644Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T04:57:29.4051028Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:57:29.4051561Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:57:29.4051866Z         
2025-03-14T04:57:29.4052385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:29.4053071Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:57:29.4053394Z         
2025-03-14T04:57:29.4053913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:29.4054588Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:57:29.4054932Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:57:29.4055268Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:57:29.4055524Z         
2025-03-14T04:57:29.4055999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:29.4056599Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:57:29.4056906Z         
2025-03-14T04:57:29.4057315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:29.4057823Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:57:29.4058090Z         
2025-03-14T04:57:29.4058494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:29.4058993Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:29.4059307Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:29.4059638Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:57:29.4059909Z         
2025-03-14T04:57:29.4060313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:29.4061040Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:29.4061344Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:29.4061678Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:57:29.4061939Z         
2025-03-14T04:57:29.4062324Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:29.4062802Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:29.4063059Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:57:29.4063305Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:57:29.4063543Z         
2025-03-14T04:57:29.4063934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:29.4064473Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:29.4064758Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:57:29.4065021Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:57:29.4065260Z         
2025-03-14T04:57:29.4065676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:29.4066177Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:29.4066499Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:57:29.4066740Z         
2025-03-14T04:57:29.4067111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:29.4067604Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:29.4067922Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:57:29.4068150Z         
2025-03-14T04:57:29.4077899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:29.4078547Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:29.4078889Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:57:29.4079138Z         
2025-03-14T04:57:29.4079547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:29.4080182Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:29.4080550Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:57:29.4080787Z         
2025-03-14T04:57:29.4081216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:29.4081750Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:29.4082011Z         
2025-03-14T04:57:29.4082433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:29.4082957Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:29.4083208Z         
2025-03-14T04:57:29.4083640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:29.4084178Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:29.4084502Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:57:29.4084839Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:29.4085194Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:57:29.4085452Z         
2025-03-14T04:57:29.4085886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:29.4086428Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:29.4086775Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:57:29.4087108Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:29.4087460Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:57:29.4087719Z         
2025-03-14T04:57:29.4088138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:29.4088640Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:29.4088973Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:29.4089324Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:57:29.4089581Z         
2025-03-14T04:57:29.4090014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:29.4090532Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:29.4090888Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:29.4091246Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:57:29.4091601Z         
2025-03-14T04:57:29.4092060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:29.4092545Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:29.4092844Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:29.4093084Z         
2025-03-14T04:57:29.4093491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:29.4093948Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:29.4094210Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:29.4094446Z         
2025-03-14T04:57:29.4094829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:29.4095310Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:29.4095599Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:29.4095847Z         
2025-03-14T04:57:29.4096237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:29.4096707Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:29.4097005Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:29.4097248Z         
2025-03-14T04:57:29.4097666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:29.4098233Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:29.4098519Z         
2025-03-14T04:57:29.4098922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:29.4099460Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:57:29.4099752Z         
2025-03-14T04:57:29.4100212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:29.4100824Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:57:29.4101109Z         
2025-03-14T04:57:29.4101679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:57:29.4102363Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:57:29.4102612Z         
2025-03-14T04:57:29.4103003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:29.4103497Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:57:29.4103755Z         
2025-03-14T04:57:29.4104275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:29.4104869Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:57:29.4105137Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:57:29.4105417Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:57:29.4105644Z         
2025-03-14T04:57:29.4106185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:29.4106878Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:57:29.4107339Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:57:29.4107781Z         
2025-03-14T04:57:29.4108331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:29.4109012Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:29.4109294Z         
2025-03-14T04:57:29.4109670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:29.4110170Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:57:29.4110440Z         
2025-03-14T04:57:29.4110903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:57:29.4111486Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:57:29.4111741Z         
2025-03-14T04:57:29.4112120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:29.4112607Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:57:29.4112869Z         
2025-03-14T04:57:29.4113333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:57:29.4113916Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:57:29.4114171Z         
2025-03-14T04:57:29.4114736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:57:29.4115401Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:57:29.4115713Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:29.4116036Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:57:29.4116379Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:57:29.4116638Z         
2025-03-14T04:57:29.4117096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:57:29.4117647Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:57:29.4117876Z         
2025-03-14T04:57:29.4117967Z 
2025-03-14T04:57:29.4118053Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:29.4119394Z     def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:57:29.4121764Z         l_features_res4_ = L_features_res4_
2025-03-14T04:57:29.4122167Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:57:29.4122682Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:57:29.4123150Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:57:29.4123673Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:57:29.4124243Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:57:29.4124802Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:57:29.4125333Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:57:29.4125685Z         
2025-03-14T04:57:29.4126221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:29.4126862Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:57:29.4127126Z         
2025-03-14T04:57:29.4127499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:29.4127982Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:57:29.4128247Z         
2025-03-14T04:57:29.4128771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:29.4129409Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:57:29.4129669Z         
2025-03-14T04:57:29.4130035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:29.4130503Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:57:29.4130755Z         
2025-03-14T04:57:29.4131202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:29.4131902Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:57:29.4132246Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:57:29.4132525Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:57:29.4132775Z         
2025-03-14T04:57:29.4133192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:29.4133726Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:57:29.4133970Z         
2025-03-14T04:57:29.4134387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:29.4134907Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:57:29.4135147Z         
2025-03-14T04:57:29.4135633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:29.4136275Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:57:29.4136600Z         
2025-03-14T04:57:29.4137188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:29.4137774Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:57:29.4138253Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:57:29.4138721Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:57:29.4139004Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:57:29.4139229Z         
2025-03-14T04:57:29.4139600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:29.4140055Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:57:29.4140286Z         
2025-03-14T04:57:29.4140617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:29.4141505Z         x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:57:29.4142254Z         
2025-03-14T04:57:29.4142606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:29.4143105Z         x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:57:29.4143399Z         
2025-03-14T04:57:29.4143855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:29.4144923Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:57:29.4145663Z         
2025-03-14T04:57:29.4146097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:29.4147229Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:57:29.4147947Z         
2025-03-14T04:57:29.4148354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:57:29.4148904Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:57:29.4149233Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:57:29.4149486Z         
2025-03-14T04:57:29.4149984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:57:29.4150595Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T04:57:29.4150971Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:57:29.4151366Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:57:29.4151657Z         
2025-03-14T04:57:29.4152138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:29.4152780Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:57:29.4153091Z         
2025-03-14T04:57:29.4153596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:29.4154221Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:57:29.4154565Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:57:29.4154901Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:57:29.4155179Z         
2025-03-14T04:57:29.4155642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:29.4156243Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:57:29.4156538Z         
2025-03-14T04:57:29.4156940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:29.4157454Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:57:29.4157721Z         
2025-03-14T04:57:29.4158128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:29.4158631Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:29.4158947Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:29.4159277Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:57:29.4159548Z         
2025-03-14T04:57:29.4159950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:29.4160464Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:29.4160972Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:29.4161299Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:57:29.4161621Z         
2025-03-14T04:57:29.4162018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:29.4162531Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:29.4162801Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:57:29.4163064Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:57:29.4163306Z         
2025-03-14T04:57:29.4163707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:29.4164213Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:29.4164502Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:57:29.4164772Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:57:29.4165015Z         
2025-03-14T04:57:29.4165419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:29.4165911Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:29.4166232Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:57:29.4166459Z         
2025-03-14T04:57:29.4166830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:29.4167319Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:29.4167633Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:57:29.4167858Z         
2025-03-14T04:57:29.4168227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:29.4168749Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:29.4169075Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:57:29.4169299Z         
2025-03-14T04:57:29.4169681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:29.4170219Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:29.4170566Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:57:29.4170796Z         
2025-03-14T04:57:29.4171216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:29.4171804Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:29.4172067Z         
2025-03-14T04:57:29.4172477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:29.4172989Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:29.4173240Z         
2025-03-14T04:57:29.4173700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:29.4174240Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:29.4174577Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:57:29.4174909Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:29.4175279Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:57:29.4175537Z         
2025-03-14T04:57:29.4175979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:29.4176537Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:29.4176864Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:57:29.4177203Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:29.4177559Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:57:29.4177817Z         
2025-03-14T04:57:29.4178243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:29.4178753Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:29.4179082Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:29.4179432Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:57:29.4179684Z         
2025-03-14T04:57:29.4180105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:29.4180611Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:29.4180948Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:29.4181315Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:57:29.4181579Z         
2025-03-14T04:57:29.4181969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:29.4182425Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:29.4182681Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:29.4182912Z         
2025-03-14T04:57:29.4183304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:29.4183751Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:29.4184005Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:29.4184236Z         
2025-03-14T04:57:29.4184622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:29.4185093Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:29.4185382Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:29.4185617Z         
2025-03-14T04:57:29.4185999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:29.4186478Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:29.4186762Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:29.4187004Z         
2025-03-14T04:57:29.4187425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:29.4188052Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:29.4188338Z         
2025-03-14T04:57:29.4188755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:29.4189297Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:57:29.4189569Z         
2025-03-14T04:57:29.4190032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:29.4190636Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:57:29.4190925Z         
2025-03-14T04:57:29.4191492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:57:29.4192165Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:57:29.4192418Z         
2025-03-14T04:57:29.4192796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:29.4193285Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:57:29.4193542Z         
2025-03-14T04:57:29.4194064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:29.4194668Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:57:29.4194958Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:57:29.4195229Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:57:29.4195460Z         
2025-03-14T04:57:29.4196005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:29.4196681Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:57:29.4197129Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:57:29.4197470Z         
2025-03-14T04:57:29.4198004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:29.4198675Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:29.4198952Z         
2025-03-14T04:57:29.4199329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:29.4199821Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:57:29.4200085Z         
2025-03-14T04:57:29.4200560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:57:29.4201159Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:57:29.4201422Z         
2025-03-14T04:57:29.4201815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:29.4202315Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:57:29.4202576Z         
2025-03-14T04:57:29.4203063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:57:29.4203628Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:57:29.4203887Z         
2025-03-14T04:57:29.4204452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:57:29.4205123Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:57:29.4205431Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:29.4205753Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:57:29.4206086Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:57:29.4206332Z         
2025-03-14T04:57:29.4206779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:57:29.4207315Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:57:29.4207544Z         
2025-03-14T04:57:29.9618464Z 
2025-03-14T04:57:29.9619103Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:29.9619780Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"):
2025-03-14T04:57:29.9620877Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T04:57:29.9621185Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T04:57:29.9621532Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T04:57:29.9621851Z         
2025-03-14T04:57:29.9622502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:29.9623257Z         pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T04:57:29.9623600Z         
2025-03-14T04:57:29.9624156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:29.9625317Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T04:57:29.9625778Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:57:29.9626151Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:57:29.9626431Z         
2025-03-14T04:57:29.9627053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:29.9627738Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T04:57:29.9628044Z         
2025-03-14T04:57:29.9628531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:29.9629116Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:57:29.9629394Z         
2025-03-14T04:57:29.9629826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:29.9630383Z         getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:29.9630711Z         getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:29.9631057Z         widths: "f32[328500][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T04:57:29.9631331Z         
2025-03-14T04:57:29.9631774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:29.9632346Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:29.9632664Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:29.9633029Z         heights: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:57:29.9633309Z         
2025-03-14T04:57:29.9633713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:29.9634212Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:29.9634489Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:57:29.9635354Z         ctr_x: "f32[328500][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T04:57:29.9635622Z         
2025-03-14T04:57:29.9636044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:29.9636577Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:29.9636914Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:57:29.9637191Z         ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T04:57:29.9637431Z         
2025-03-14T04:57:29.9637867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:29.9638384Z         getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:29.9638721Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T04:57:29.9638957Z         
2025-03-14T04:57:29.9639347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:29.9639896Z         getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:29.9640226Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T04:57:29.9640462Z         
2025-03-14T04:57:29.9640851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:29.9641887Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:29.9642223Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:57:29.9642496Z         
2025-03-14T04:57:29.9642899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:29.9643473Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:29.9643837Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:57:29.9644078Z         
2025-03-14T04:57:29.9644539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:29.9645079Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:29.9645335Z         
2025-03-14T04:57:29.9645755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:29.9646281Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:29.9646535Z         
2025-03-14T04:57:29.9646971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:29.9647525Z         getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:29.9647851Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T04:57:29.9648189Z         getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:29.9648545Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T04:57:29.9648807Z         
2025-03-14T04:57:29.9649249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:29.9649795Z         getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:29.9650121Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T04:57:29.9650479Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:29.9650836Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T04:57:29.9651100Z         
2025-03-14T04:57:29.9651732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:29.9652312Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:29.9652682Z         getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:29.9653066Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T04:57:29.9653347Z         
2025-03-14T04:57:29.9653818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:29.9654335Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:29.9654674Z         getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:29.9655051Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T04:57:29.9655330Z         
2025-03-14T04:57:29.9655767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:29.9656303Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:29.9656599Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:29.9656916Z         
2025-03-14T04:57:29.9657349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:29.9657856Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:29.9658166Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:29.9658430Z         
2025-03-14T04:57:29.9658863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:29.9659390Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:29.9659714Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:29.9659987Z         
2025-03-14T04:57:29.9660454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:29.9661249Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:29.9661541Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:29.9661785Z         
2025-03-14T04:57:29.9662202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:29.9662774Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:29.9663066Z         
2025-03-14T04:57:29.9663477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:29.9664015Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:57:29.9664292Z         
2025-03-14T04:57:29.9664751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:29.9665421Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:57:29.9665706Z         
2025-03-14T04:57:29.9666274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:57:29.9666970Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:57:29.9667220Z         
2025-03-14T04:57:29.9667609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:29.9668106Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:57:29.9668352Z         
2025-03-14T04:57:29.9668870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:29.9669523Z         topk = l_pred_objectness_logits_0_.topk(6000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T04:57:29.9669852Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:57:29.9670115Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:57:29.9670343Z         
2025-03-14T04:57:29.9670926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:29.9671627Z         getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:57:29.9672101Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)];  proposals_i_1 = getitem_18 = topk_idx = None
2025-03-14T04:57:29.9672445Z         
2025-03-14T04:57:29.9672981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:29.9673653Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:29.9673936Z         
2025-03-14T04:57:29.9674316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:29.9674809Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:57:29.9675075Z         
2025-03-14T04:57:29.9675538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:57:29.9676113Z         getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:57:29.9676375Z         
2025-03-14T04:57:29.9676754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:29.9677246Z         tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32);  getitem_20 = None
2025-03-14T04:57:29.9677506Z         
2025-03-14T04:57:29.9677961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:57:29.9678526Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:57:29.9678803Z         
2025-03-14T04:57:29.9679369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:57:29.9680030Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T04:57:29.9680334Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:29.9680663Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:57:29.9681000Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:57:29.9681248Z         
2025-03-14T04:57:29.9681690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:57:29.9682224Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:57:29.9682456Z         
2025-03-14T04:57:43.3861588Z 
2025-03-14T04:57:43.3862269Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:43.3864332Z     def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"):
2025-03-14T04:57:43.3865864Z         l_stack0_ = L_stack0_
2025-03-14T04:57:43.3866318Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:57:43.3866888Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:57:43.3867445Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:57:43.3868006Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:57:43.3868489Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:57:43.3868895Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:57:43.3869294Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:57:43.3869692Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:57:43.3870006Z         
2025-03-14T04:57:43.3870563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T04:57:43.3871199Z         mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T04:57:43.3871461Z         
2025-03-14T04:57:43.3871855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:57:43.3872843Z         scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:57:43.3873605Z         
2025-03-14T04:57:43.3874016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:57:43.3875030Z         proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:57:43.3875782Z         
2025-03-14T04:57:43.3876158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:57:43.3876630Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:57:43.3876882Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:57:43.3877116Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:57:43.3877389Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:57:43.3877650Z         getitem_2: "Sym(1231 - s0)" = size_1[0]
2025-03-14T04:57:43.3877893Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:57:43.3878100Z         
2025-03-14T04:57:43.3878487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:43.3879449Z         proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:57:43.3880187Z         
2025-03-14T04:57:43.3880667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:43.3881237Z         deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:57:43.3881513Z         
2025-03-14T04:57:43.3881897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:43.3882413Z         boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:57:43.3882678Z         
2025-03-14T04:57:43.3883074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:43.3883570Z         getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:43.3883873Z         getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:43.3884188Z         widths: "f32[3231][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:57:43.3884450Z         
2025-03-14T04:57:43.3884851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:43.3885343Z         getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:43.3885633Z         getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:43.3885947Z         heights: "f32[3231][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:57:43.3886211Z         
2025-03-14T04:57:43.3886603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:43.3887107Z         getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:43.3887358Z         mul: "f32[3231][1]cpu" = 0.5 * widths
2025-03-14T04:57:43.3887612Z         ctr_x: "f32[3231][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:57:43.3887846Z         
2025-03-14T04:57:43.3888235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:43.3888732Z         getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:43.3889010Z         mul_1: "f32[3231][1]cpu" = 0.5 * heights
2025-03-14T04:57:43.3889269Z         ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:57:43.3889507Z         
2025-03-14T04:57:43.3890037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:43.3890551Z         getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:43.3890875Z         dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:57:43.3891108Z         
2025-03-14T04:57:43.3891602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:43.3892173Z         getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:43.3892516Z         dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:57:43.3892784Z         
2025-03-14T04:57:43.3893167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:43.3893687Z         getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:43.3894014Z         dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:57:43.3894248Z         
2025-03-14T04:57:43.3894649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:43.3895195Z         getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:43.3895547Z         dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:57:43.3895789Z         
2025-03-14T04:57:43.3896205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:43.3896734Z         dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:43.3896983Z         
2025-03-14T04:57:43.3897395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:43.3897912Z         dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:43.3898157Z         
2025-03-14T04:57:43.3898589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:43.3899126Z         getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:43.3899439Z         mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:57:43.3899762Z         getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:43.3900123Z         pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:57:43.3900373Z         
2025-03-14T04:57:43.3900802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:43.3901337Z         getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:43.3901650Z         mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:57:43.3901974Z         getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:43.3902311Z         pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:57:43.3902563Z         
2025-03-14T04:57:43.3902986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:43.3903486Z         exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:43.3903808Z         getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:43.3904147Z         pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:57:43.3904390Z         
2025-03-14T04:57:43.3904825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:43.3905321Z         exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:43.3905662Z         getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:43.3906002Z         pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:57:43.3906254Z         
2025-03-14T04:57:43.3906700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:43.3907175Z         mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:43.3907440Z         x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:43.3907679Z         
2025-03-14T04:57:43.3908082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:43.3908548Z         mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:43.3908812Z         y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:43.3909051Z         
2025-03-14T04:57:43.3909453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:43.3909937Z         mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:43.3910231Z         x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:43.3910481Z         
2025-03-14T04:57:43.3910882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:43.3911365Z         mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:43.3911661Z         y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:43.3911901Z         
2025-03-14T04:57:43.3912328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:43.3912931Z         pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:43.3913223Z         
2025-03-14T04:57:43.3913649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:43.3914219Z         predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320));  pred_boxes = None
2025-03-14T04:57:43.3914508Z         
2025-03-14T04:57:43.3914959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:57:43.3915598Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:57:43.3915971Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:57:43.3916261Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:57:43.3916571Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:57:43.3916889Z         getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:57:43.3917159Z         
2025-03-14T04:57:43.3917532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:57:43.3918104Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:57:43.3918452Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:57:43.3918689Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:57:43.3919067Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:57:43.3919412Z         getitem_26: "Sym(1231 - s0)" = size_3[0]
2025-03-14T04:57:43.3919668Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:57:43.3919884Z         
2025-03-14T04:57:43.3920298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:57:43.3920847Z         probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:57:43.3921127Z         
2025-03-14T04:57:43.3921563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:57:43.3922153Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:57:43.3922511Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:57:43.3922796Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:57:43.3923091Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:57:43.3923398Z         getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:57:43.3923654Z         
2025-03-14T04:57:43.3924197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:57:43.3924885Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:57:43.3925220Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:43.3925553Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:57:43.3925885Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:57:43.3926194Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:57:43.3926429Z         
2025-03-14T04:57:43.3926861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:57:43.3927368Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:57:43.3927599Z         
2025-03-14T04:57:43.3927736Z 
2025-03-14T04:57:43.3927823Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:43.3929203Z     def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"):
2025-03-14T04:57:43.3930552Z         l_stack0_ = L_stack0_
2025-03-14T04:57:43.3930948Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:57:43.3931596Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:57:43.3932167Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:57:43.3932771Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:57:43.3933261Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:57:43.3933668Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:57:43.3934071Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:57:43.3934460Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:57:43.3934750Z         
2025-03-14T04:57:43.3935293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T04:57:43.3935949Z         mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T04:57:43.3936219Z         
2025-03-14T04:57:43.3936618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:57:43.3937624Z         scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:57:43.3938361Z         
2025-03-14T04:57:43.3938778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:57:43.3939824Z         proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:57:43.3940604Z         
2025-03-14T04:57:43.3940990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:57:43.3941458Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:57:43.3941714Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:57:43.3941950Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:57:43.3942229Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:57:43.3942496Z         getitem_2: "Sym(1231 - s0)" = size_1[0]
2025-03-14T04:57:43.3942742Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:57:43.3942965Z         
2025-03-14T04:57:43.3943341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:43.3944291Z         proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:57:43.3945030Z         
2025-03-14T04:57:43.3945478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:43.3946041Z         deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:57:43.3946324Z         
2025-03-14T04:57:43.3946717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:43.3947247Z         boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:57:43.3947511Z         
2025-03-14T04:57:43.3947894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:43.3948369Z         getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:43.3948661Z         getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:43.3948966Z         widths: "f32[3231][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:57:43.3949215Z         
2025-03-14T04:57:43.3949603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:43.3950080Z         getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:43.3950363Z         getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:43.3950668Z         heights: "f32[3231][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:57:43.3950917Z         
2025-03-14T04:57:43.3951299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:43.3951768Z         getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:43.3952019Z         mul: "f32[3231][1]cpu" = 0.5 * widths
2025-03-14T04:57:43.3952270Z         ctr_x: "f32[3231][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:57:43.3952499Z         
2025-03-14T04:57:43.3952880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:43.3953390Z         getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:43.3953662Z         mul_1: "f32[3231][1]cpu" = 0.5 * heights
2025-03-14T04:57:43.3953914Z         ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:57:43.3954146Z         
2025-03-14T04:57:43.3954529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:43.3955024Z         getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:43.3955339Z         dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:57:43.3955563Z         
2025-03-14T04:57:43.3955939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:43.3956423Z         getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:43.3956736Z         dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:57:43.3956964Z         
2025-03-14T04:57:43.3957343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:43.3957839Z         getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:43.3958170Z         dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:57:43.3958397Z         
2025-03-14T04:57:43.3958781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:43.3959344Z         getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:43.3959705Z         dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:57:43.3959933Z         
2025-03-14T04:57:43.3960356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:43.3961247Z         dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:43.3961598Z         
2025-03-14T04:57:43.3962023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:43.3962545Z         dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:43.3962795Z         
2025-03-14T04:57:43.3963212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:43.3963740Z         getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:43.3964046Z         mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:57:43.3964369Z         getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:43.3964706Z         pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:57:43.3964955Z         
2025-03-14T04:57:43.3965381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:43.3965921Z         getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:43.3966235Z         mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:57:43.3966625Z         getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:43.3966980Z         pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:57:43.3967239Z         
2025-03-14T04:57:43.3967678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:43.3968176Z         exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:43.3968497Z         getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:43.3968831Z         pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:57:43.3969079Z         
2025-03-14T04:57:43.3969494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:43.3969992Z         exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:43.3970316Z         getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:43.3970657Z         pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:57:43.3970906Z         
2025-03-14T04:57:43.3971327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:43.3971842Z         mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:43.3972104Z         x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:43.3972388Z         
2025-03-14T04:57:43.3972794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:43.3973302Z         mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:43.3973556Z         y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:43.3973783Z         
2025-03-14T04:57:43.3974169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:43.3974639Z         mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:43.3974927Z         x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:43.3975169Z         
2025-03-14T04:57:43.3975555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:43.3976023Z         mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:43.3976305Z         y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:43.3976545Z         
2025-03-14T04:57:43.3976972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:43.3977548Z         pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:43.3977833Z         
2025-03-14T04:57:43.3978257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:43.3978808Z         predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320));  pred_boxes = None
2025-03-14T04:57:43.3979083Z         
2025-03-14T04:57:43.3979530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:57:43.3980148Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:57:43.3980504Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:57:43.3980789Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:57:43.3981089Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:57:43.3981403Z         getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:57:43.3981660Z         
2025-03-14T04:57:43.3982038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:57:43.3982590Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:57:43.3982935Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:57:43.3983173Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:57:43.3983528Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:57:43.3983877Z         getitem_26: "Sym(1231 - s0)" = size_3[0]
2025-03-14T04:57:43.3984112Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:57:43.3984324Z         
2025-03-14T04:57:43.3984751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:57:43.3985300Z         probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:57:43.3985593Z         
2025-03-14T04:57:43.3986029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:57:43.3986612Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:57:43.3986967Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:57:43.3987254Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:57:43.3987546Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:57:43.3987857Z         getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:57:43.3988110Z         
2025-03-14T04:57:43.3988656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:57:43.3989324Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:57:43.3989652Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:43.3989982Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:57:43.3990308Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:57:43.3990585Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:57:43.3990814Z         
2025-03-14T04:57:43.3991232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:57:43.3991729Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:57:43.3991955Z         
2025-03-14T04:57:43.3992102Z 
2025-03-14T04:57:43.3992187Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:43.3993540Z     def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"):
2025-03-14T04:57:43.3994876Z         l_stack0_ = L_stack0_
2025-03-14T04:57:43.3995256Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:57:43.3995816Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:57:43.3996367Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:57:43.3996913Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:57:43.3997398Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:57:43.3997791Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:57:43.3998175Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:57:43.3998577Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:57:43.3998863Z         
2025-03-14T04:57:43.3999394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T04:57:43.4000024Z         mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T04:57:43.4000279Z         
2025-03-14T04:57:43.4000669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:57:43.4001633Z         scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:57:43.4002350Z         
2025-03-14T04:57:43.4002754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:57:43.4003751Z         proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:57:43.4004486Z         
2025-03-14T04:57:43.4004856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:57:43.4005316Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:57:43.4005567Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:57:43.4005812Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:57:43.4006081Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:57:43.4006333Z         getitem_2: "Sym(1231 - s0)" = size_1[0]
2025-03-14T04:57:43.4006581Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:57:43.4006812Z         
2025-03-14T04:57:43.4007195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:43.4008172Z         proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:57:43.4008970Z         
2025-03-14T04:57:43.4009477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:43.4010111Z         deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:57:43.4010407Z         
2025-03-14T04:57:43.4010835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:43.4011529Z         boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:57:43.4011839Z         
2025-03-14T04:57:43.4012280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:43.4012850Z         getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:43.4013168Z         getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:43.4013514Z         widths: "f32[3231][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:57:43.4013790Z         
2025-03-14T04:57:43.4014219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:43.4014740Z         getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:43.4015046Z         getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:43.4015378Z         heights: "f32[3231][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:57:43.4015652Z         
2025-03-14T04:57:43.4016065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:43.4016576Z         getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:43.4016844Z         mul: "f32[3231][1]cpu" = 0.5 * widths
2025-03-14T04:57:43.4017110Z         ctr_x: "f32[3231][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:57:43.4017360Z         
2025-03-14T04:57:43.4017782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:43.4018313Z         getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:43.4018610Z         mul_1: "f32[3231][1]cpu" = 0.5 * heights
2025-03-14T04:57:43.4018882Z         ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:57:43.4019137Z         
2025-03-14T04:57:43.4019562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:43.4020121Z         getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:43.4020459Z         dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:57:43.4020706Z         
2025-03-14T04:57:43.4021109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:43.4021634Z         getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:43.4021964Z         dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:57:43.4022185Z         
2025-03-14T04:57:43.4022563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:43.4023059Z         getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:43.4023376Z         dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:57:43.4023606Z         
2025-03-14T04:57:43.4023986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:43.4024513Z         getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:43.4024854Z         dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:57:43.4025080Z         
2025-03-14T04:57:43.4025513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:43.4026055Z         dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:43.4026303Z         
2025-03-14T04:57:43.4026735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:43.4027248Z         dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:43.4027492Z         
2025-03-14T04:57:43.4027913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:43.4028445Z         getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:43.4028753Z         mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:57:43.4029071Z         getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:43.4029413Z         pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:57:43.4029663Z         
2025-03-14T04:57:43.4030095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:43.4030627Z         getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:43.4030932Z         mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:57:43.4031249Z         getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:43.4031585Z         pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:57:43.4031833Z         
2025-03-14T04:57:43.4032244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:43.4032756Z         exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:43.4033075Z         getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:43.4033417Z         pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:57:43.4033654Z         
2025-03-14T04:57:43.4034051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:43.4034536Z         exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:43.4034859Z         getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:43.4035202Z         pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:57:43.4035456Z         
2025-03-14T04:57:43.4035847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:43.4036306Z         mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:43.4036563Z         x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:43.4036792Z         
2025-03-14T04:57:43.4037183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:43.4037635Z         mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:43.4037906Z         y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:43.4038139Z         
2025-03-14T04:57:43.4038530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:43.4039019Z         mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:43.4039327Z         x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:43.4039569Z         
2025-03-14T04:57:43.4039952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:43.4040420Z         mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:43.4040703Z         y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:43.4040944Z         
2025-03-14T04:57:43.4041374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:43.4041973Z         pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:43.4042276Z         
2025-03-14T04:57:43.4042716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:43.4043298Z         predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320));  pred_boxes = None
2025-03-14T04:57:43.4043593Z         
2025-03-14T04:57:43.4044060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:57:43.4044675Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:57:43.4045034Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:57:43.4045313Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:57:43.4045613Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:57:43.4045949Z         getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:57:43.4046203Z         
2025-03-14T04:57:43.4046576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:57:43.4047116Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:57:43.4047459Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:57:43.4047696Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:57:43.4048051Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:57:43.4048395Z         getitem_26: "Sym(1231 - s0)" = size_3[0]
2025-03-14T04:57:43.4048639Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:57:43.4048855Z         
2025-03-14T04:57:43.4049267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:57:43.4049816Z         probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:57:43.4050096Z         
2025-03-14T04:57:43.4050529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:57:43.4051145Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:57:43.4051621Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:57:43.4051972Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:57:43.4052310Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:57:43.4052690Z         getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:57:43.4052986Z         
2025-03-14T04:57:43.4053550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:57:43.4054239Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:57:43.4054576Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:43.4054912Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:57:43.4055248Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:57:43.4055539Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:57:43.4055772Z         
2025-03-14T04:57:43.4056209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:57:43.4056721Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:57:43.4056946Z         
2025-03-14T04:57:43.4057079Z 
2025-03-14T04:57:43.4057165Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:43.4058529Z     def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"):
2025-03-14T04:57:43.4059901Z         l_stack0_ = L_stack0_
2025-03-14T04:57:43.4060291Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:57:43.4061256Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:57:43.4061840Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:57:43.4062404Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:57:43.4062893Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:57:43.4063298Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:57:43.4063698Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:57:43.4064092Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:57:43.4064382Z         
2025-03-14T04:57:43.4064977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T04:57:43.4065623Z         mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T04:57:43.4065915Z         
2025-03-14T04:57:43.4066309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:57:43.4067339Z         scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:57:43.4068071Z         
2025-03-14T04:57:43.4068488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:57:43.4069491Z         proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:57:43.4070224Z         
2025-03-14T04:57:43.4070596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:57:43.4071049Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:57:43.4071296Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:57:43.4071523Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:57:43.4071791Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:57:43.4072050Z         getitem_2: "Sym(1231 - s0)" = size_1[0]
2025-03-14T04:57:43.4072291Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:57:43.4072501Z         
2025-03-14T04:57:43.4072855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:43.4073893Z         proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:57:43.4074593Z         
2025-03-14T04:57:43.4075028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:43.4075590Z         deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:57:43.4075844Z         
2025-03-14T04:57:43.4076233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:43.4076734Z         boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:57:43.4077002Z         
2025-03-14T04:57:43.4077386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:43.4077862Z         getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:43.4078149Z         getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:43.4078458Z         widths: "f32[3231][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:57:43.4078725Z         
2025-03-14T04:57:43.4079119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:43.4079610Z         getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:43.4079890Z         getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:43.4080209Z         heights: "f32[3231][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:57:43.4080461Z         
2025-03-14T04:57:43.4080848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:43.4081316Z         getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:43.4081565Z         mul: "f32[3231][1]cpu" = 0.5 * widths
2025-03-14T04:57:43.4081815Z         ctr_x: "f32[3231][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:57:43.4082040Z         
2025-03-14T04:57:43.4082422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:43.4082912Z         getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:43.4083186Z         mul_1: "f32[3231][1]cpu" = 0.5 * heights
2025-03-14T04:57:43.4083437Z         ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:57:43.4083666Z         
2025-03-14T04:57:43.4084051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:43.4084545Z         getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:43.4084858Z         dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:57:43.4085083Z         
2025-03-14T04:57:43.4085454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:43.4085936Z         getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:43.4086264Z         dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:57:43.4086495Z         
2025-03-14T04:57:43.4086866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:43.4087352Z         getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:43.4087662Z         dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:57:43.4087893Z         
2025-03-14T04:57:43.4088269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:43.4088787Z         getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:43.4089128Z         dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:57:43.4089359Z         
2025-03-14T04:57:43.4089776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:43.4090297Z         dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:43.4090552Z         
2025-03-14T04:57:43.4090964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:43.4091586Z         dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:43.4091853Z         
2025-03-14T04:57:43.4092304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:43.4092886Z         getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:43.4093235Z         mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:57:43.4093579Z         getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:43.4093946Z         pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:57:43.4094199Z         
2025-03-14T04:57:43.4094627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:43.4095156Z         getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:43.4095463Z         mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:57:43.4095786Z         getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:43.4096127Z         pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:57:43.4096375Z         
2025-03-14T04:57:43.4096789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:43.4097284Z         exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:43.4097602Z         getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:43.4097940Z         pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:57:43.4098188Z         
2025-03-14T04:57:43.4098603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:43.4099121Z         exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:43.4099447Z         getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:43.4099793Z         pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:57:43.4100046Z         
2025-03-14T04:57:43.4100438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:43.4100897Z         mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:43.4101152Z         x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:43.4101382Z         
2025-03-14T04:57:43.4101776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:43.4102239Z         mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:43.4102494Z         y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:43.4102719Z         
2025-03-14T04:57:43.4103116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:43.4103572Z         mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:43.4103848Z         x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:43.4104089Z         
2025-03-14T04:57:43.4104487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:43.4104958Z         mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:43.4105256Z         y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:43.4105502Z         
2025-03-14T04:57:43.4105957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:43.4106530Z         pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:43.4106813Z         
2025-03-14T04:57:43.4107223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:43.4107775Z         predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320));  pred_boxes = None
2025-03-14T04:57:43.4108048Z         
2025-03-14T04:57:43.4108488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:57:43.4109100Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:57:43.4109457Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:57:43.4109741Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:57:43.4110037Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:57:43.4110349Z         getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:57:43.4110606Z         
2025-03-14T04:57:43.4110979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:57:43.4111518Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:57:43.4111864Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:57:43.4112118Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:57:43.4112477Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:57:43.4112822Z         getitem_26: "Sym(1231 - s0)" = size_3[0]
2025-03-14T04:57:43.4113062Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:57:43.4113278Z         
2025-03-14T04:57:43.4113689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:57:43.4114242Z         probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:57:43.4114515Z         
2025-03-14T04:57:43.4114936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:57:43.4115521Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:57:43.4115870Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:57:43.4116155Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:57:43.4116451Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:57:43.4116760Z         getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:57:43.4117009Z         
2025-03-14T04:57:43.4117573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:57:43.4118272Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:57:43.4118603Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:43.4118952Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:57:43.4119291Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:57:43.4119580Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:57:43.4119814Z         
2025-03-14T04:57:43.4120250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:57:43.4120761Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:57:43.4120990Z         
2025-03-14T04:57:44.9334320Z 
2025-03-14T04:57:44.9337180Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:44.9338322Z     def forward(self, L_predictions_0_: "f32[3231, 81][81, 1]cpu", L_predictions_1_: "f32[3231, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"):
2025-03-14T04:57:44.9339357Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:57:44.9339610Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:57:44.9340011Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:57:44.9340511Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:57:44.9340976Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:57:44.9341393Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:57:44.9341994Z         
2025-03-14T04:57:44.9342431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:57:44.9342931Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:57:44.9343292Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:57:44.9343642Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:57:44.9343998Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:57:44.9344267Z         getitem_2: "Sym(1231 - s0)" = size_1[0]
2025-03-14T04:57:44.9344517Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:57:44.9344737Z         
2025-03-14T04:57:44.9345122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:44.9346107Z         proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:57:44.9346864Z         
2025-03-14T04:57:44.9347342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:44.9348026Z         deltas: "f32[3231, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:57:44.9348309Z         
2025-03-14T04:57:44.9348738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:44.9349335Z         boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:57:44.9349616Z         
2025-03-14T04:57:44.9350062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:44.9350579Z         getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:44.9350888Z         getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:44.9351245Z         widths: "f32[3231][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:57:44.9351511Z         
2025-03-14T04:57:44.9351931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:44.9352444Z         getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:44.9352746Z         getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:44.9353068Z         heights: "f32[3231][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:57:44.9353342Z         
2025-03-14T04:57:44.9353747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:44.9354248Z         getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:44.9354512Z         mul: "f32[3231][1]cpu" = 0.5 * widths
2025-03-14T04:57:44.9354777Z         ctr_x: "f32[3231][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:57:44.9355027Z         
2025-03-14T04:57:44.9355439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:44.9355933Z         getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:44.9356213Z         mul_1: "f32[3231][1]cpu" = 0.5 * heights
2025-03-14T04:57:44.9356495Z         ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:57:44.9356732Z         
2025-03-14T04:57:44.9357151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:44.9357656Z         getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:44.9357975Z         dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:57:44.9358202Z         
2025-03-14T04:57:44.9358587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:44.9359083Z         getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:44.9359399Z         dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:57:44.9359633Z         
2025-03-14T04:57:44.9360013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:44.9360504Z         getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:44.9361032Z         dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:57:44.9361261Z         
2025-03-14T04:57:44.9361685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:44.9362217Z         getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:44.9362595Z         dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:57:44.9362834Z         
2025-03-14T04:57:44.9363286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:44.9363812Z         dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:44.9364060Z         
2025-03-14T04:57:44.9364458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:44.9364958Z         dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:44.9365201Z         
2025-03-14T04:57:44.9365626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:44.9366157Z         getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:44.9366466Z         mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:57:44.9366791Z         getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:44.9367131Z         pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:57:44.9367374Z         
2025-03-14T04:57:44.9367796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:44.9368325Z         getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:44.9368633Z         mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:57:44.9368954Z         getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:44.9369292Z         pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:57:44.9369568Z         
2025-03-14T04:57:44.9369992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:44.9370491Z         exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:44.9370813Z         getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:44.9371149Z         pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:57:44.9371553Z         
2025-03-14T04:57:44.9372014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:44.9372546Z         exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:44.9372898Z         getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:44.9373264Z         pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:57:44.9373516Z         
2025-03-14T04:57:44.9373921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:44.9374379Z         mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:44.9374656Z         x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:44.9374887Z         
2025-03-14T04:57:44.9375276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:44.9375741Z         mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:44.9375992Z         y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:44.9376223Z         
2025-03-14T04:57:44.9376624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:44.9377095Z         mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:44.9377382Z         x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:44.9377623Z         
2025-03-14T04:57:44.9378007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:44.9378471Z         mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:44.9378752Z         y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:44.9378995Z         
2025-03-14T04:57:44.9379511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:44.9380332Z         pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:44.9380621Z         
2025-03-14T04:57:44.9381039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:44.9381595Z         predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320));  pred_boxes = None
2025-03-14T04:57:44.9381880Z         
2025-03-14T04:57:44.9382339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:57:44.9382970Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:57:44.9383432Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:57:44.9383885Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:57:44.9384189Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:57:44.9384497Z         getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:57:44.9384756Z         
2025-03-14T04:57:44.9385135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:57:44.9385698Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:57:44.9386046Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:57:44.9386290Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:57:44.9386666Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:57:44.9387027Z         getitem_26: "Sym(1231 - s0)" = size_3[0]
2025-03-14T04:57:44.9387277Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:57:44.9387496Z         
2025-03-14T04:57:44.9387923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:57:44.9388560Z         probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:57:44.9388876Z         
2025-03-14T04:57:44.9389320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:57:44.9389953Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:57:44.9390381Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:57:44.9390678Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:57:44.9390980Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:57:44.9391296Z         getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:57:44.9391555Z         
2025-03-14T04:57:44.9392170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:57:44.9392878Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:57:44.9393222Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:44.9393572Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:57:44.9393910Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:57:44.9394214Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:57:44.9394456Z         
2025-03-14T04:57:44.9394905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:57:44.9395431Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:57:44.9395673Z         
2025-03-14T04:57:44.9395767Z 
2025-03-14T04:57:44.9395856Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:44.9396702Z     def forward(self, L_predictions_0_: "f32[3231, 81][81, 1]cpu", L_predictions_1_: "f32[3231, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"):
2025-03-14T04:57:44.9397529Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:57:44.9397760Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:57:44.9398077Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:57:44.9398486Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:57:44.9398885Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:57:44.9399282Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:57:44.9399573Z         
2025-03-14T04:57:44.9399961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:57:44.9400432Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:57:44.9400684Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:57:44.9400919Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:57:44.9401197Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:57:44.9401461Z         getitem_2: "Sym(1231 - s0)" = size_1[0]
2025-03-14T04:57:44.9401726Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:57:44.9401950Z         
2025-03-14T04:57:44.9402327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:44.9403327Z         proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:57:44.9404071Z         
2025-03-14T04:57:44.9404533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:44.9405113Z         deltas: "f32[3231, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:57:44.9405387Z         
2025-03-14T04:57:44.9405794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:44.9406335Z         boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:57:44.9406613Z         
2025-03-14T04:57:44.9407027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:44.9407574Z         getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:44.9407877Z         getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:44.9408193Z         widths: "f32[3231][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:57:44.9408455Z         
2025-03-14T04:57:44.9408864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:44.9409363Z         getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:44.9409658Z         getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:44.9409976Z         heights: "f32[3231][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:57:44.9410260Z         
2025-03-14T04:57:44.9410665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:44.9411161Z         getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:44.9411499Z         mul: "f32[3231][1]cpu" = 0.5 * widths
2025-03-14T04:57:44.9411777Z         ctr_x: "f32[3231][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:57:44.9412023Z         
2025-03-14T04:57:44.9412435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:44.9412973Z         getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:44.9414382Z         mul_1: "f32[3231][1]cpu" = 0.5 * heights
2025-03-14T04:57:44.9414688Z         ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:57:44.9414940Z         
2025-03-14T04:57:44.9415364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:44.9415886Z         getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:44.9416222Z         dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:57:44.9416459Z         
2025-03-14T04:57:44.9416888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:44.9417534Z         getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:44.9418444Z         dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:57:44.9418687Z         
2025-03-14T04:57:44.9419124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:44.9419629Z         getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:44.9419943Z         dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:57:44.9420169Z         
2025-03-14T04:57:44.9420560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:44.9421357Z         getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:44.9421725Z         dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:57:44.9421961Z         
2025-03-14T04:57:44.9422384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:44.9422924Z         dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:44.9423184Z         
2025-03-14T04:57:44.9423601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:44.9424117Z         dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:44.9424365Z         
2025-03-14T04:57:44.9424788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:44.9425323Z         getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:44.9425632Z         mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:57:44.9425987Z         getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:44.9426325Z         pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:57:44.9426579Z         
2025-03-14T04:57:44.9427004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:44.9427537Z         getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:44.9427846Z         mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:57:44.9428167Z         getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:44.9428513Z         pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:57:44.9428762Z         
2025-03-14T04:57:44.9429165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:44.9429655Z         exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:44.9429965Z         getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:44.9430290Z         pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:57:44.9430553Z         
2025-03-14T04:57:44.9430962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:44.9431463Z         exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:44.9431785Z         getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:44.9432152Z         pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:57:44.9432395Z         
2025-03-14T04:57:44.9432783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:44.9433226Z         mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:44.9433468Z         x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:44.9433691Z         
2025-03-14T04:57:44.9434070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:44.9434505Z         mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:44.9434750Z         y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:44.9434978Z         
2025-03-14T04:57:44.9435352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:44.9435803Z         mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:44.9436077Z         x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:44.9436312Z         
2025-03-14T04:57:44.9436694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:44.9437161Z         mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:44.9437435Z         y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:44.9437671Z         
2025-03-14T04:57:44.9438082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:44.9438654Z         pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:44.9438933Z         
2025-03-14T04:57:44.9439338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:44.9440135Z         predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320));  pred_boxes = None
2025-03-14T04:57:44.9440479Z         
2025-03-14T04:57:44.9440920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:57:44.9441519Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:57:44.9441874Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:57:44.9442153Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:57:44.9442444Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:57:44.9442751Z         getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:57:44.9443002Z         
2025-03-14T04:57:44.9444354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:57:44.9444916Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:57:44.9446839Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:57:44.9447199Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:57:44.9448371Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:57:44.9449005Z         getitem_26: "Sym(1231 - s0)" = size_3[0]
2025-03-14T04:57:44.9449268Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:57:44.9449653Z         
2025-03-14T04:57:44.9450130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:57:44.9450959Z         probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:57:44.9451505Z         
2025-03-14T04:57:44.9452021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:57:44.9452856Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:57:44.9453439Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:57:44.9453778Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:57:44.9454194Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:57:44.9454558Z         getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:57:44.9454819Z         
2025-03-14T04:57:44.9455528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:57:44.9456419Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:57:44.9456889Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:44.9457422Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:57:44.9457927Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:57:44.9458249Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:57:44.9458489Z         
2025-03-14T04:57:44.9459086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:57:44.9459788Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:57:44.9460049Z         
2025-03-14T04:57:44.9460189Z 
2025-03-14T04:57:44.9460286Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:44.9461556Z     def forward(self, L_predictions_0_: "f32[3231, 81][81, 1]cpu", L_predictions_1_: "f32[3231, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"):
2025-03-14T04:57:44.9468705Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:57:44.9468993Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:57:44.9470719Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:57:44.9471326Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:57:44.9471718Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:57:44.9472094Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:57:44.9472431Z         
2025-03-14T04:57:44.9472886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:57:44.9473387Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:57:44.9473630Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:57:44.9473856Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:57:44.9474130Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:57:44.9474382Z         getitem_2: "Sym(1231 - s0)" = size_1[0]
2025-03-14T04:57:44.9474623Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:57:44.9474848Z         
2025-03-14T04:57:44.9475215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:44.9476132Z         proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:57:44.9476851Z         
2025-03-14T04:57:44.9477302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:44.9477861Z         deltas: "f32[3231, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:57:44.9478129Z         
2025-03-14T04:57:44.9478524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:44.9479048Z         boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:57:44.9479333Z         
2025-03-14T04:57:44.9479734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:44.9480283Z         getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:44.9480586Z         getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:44.9480897Z         widths: "f32[3231][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:57:44.9481150Z         
2025-03-14T04:57:44.9481545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:44.9482035Z         getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:44.9482409Z         getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:44.9482728Z         heights: "f32[3231][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:57:44.9482988Z         
2025-03-14T04:57:44.9483381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:44.9483862Z         getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:44.9484120Z         mul: "f32[3231][1]cpu" = 0.5 * widths
2025-03-14T04:57:44.9484372Z         ctr_x: "f32[3231][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:57:44.9484602Z         
2025-03-14T04:57:44.9485021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:44.9485532Z         getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:44.9485834Z         mul_1: "f32[3231][1]cpu" = 0.5 * heights
2025-03-14T04:57:44.9486096Z         ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:57:44.9486334Z         
2025-03-14T04:57:44.9486760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:44.9487271Z         getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:44.9487596Z         dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:57:44.9487826Z         
2025-03-14T04:57:44.9488212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:44.9488717Z         getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:44.9489037Z         dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:57:44.9489264Z         
2025-03-14T04:57:44.9489642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:44.9490141Z         getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:44.9490454Z         dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:57:44.9490685Z         
2025-03-14T04:57:44.9491070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:44.9491696Z         getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:44.9492083Z         dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:57:44.9492342Z         
2025-03-14T04:57:44.9492815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:44.9493415Z         dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:44.9493676Z         
2025-03-14T04:57:44.9494121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:44.9494658Z         dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:44.9494907Z         
2025-03-14T04:57:44.9495331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:44.9495862Z         getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:44.9496179Z         mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:57:44.9496511Z         getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:44.9496855Z         pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:57:44.9497107Z         
2025-03-14T04:57:44.9497556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:44.9498107Z         getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:44.9498420Z         mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:57:44.9498742Z         getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:44.9499117Z         pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:57:44.9499378Z         
2025-03-14T04:57:44.9499822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:44.9500324Z         exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:44.9500642Z         getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:44.9500979Z         pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:57:44.9501228Z         
2025-03-14T04:57:44.9501648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:44.9502145Z         exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:44.9502474Z         getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:44.9502821Z         pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:57:44.9503072Z         
2025-03-14T04:57:44.9503470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:44.9503925Z         mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:44.9504185Z         x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:44.9504414Z         
2025-03-14T04:57:44.9504804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:44.9505256Z         mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:44.9505518Z         y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:44.9505744Z         
2025-03-14T04:57:44.9506160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:44.9506640Z         mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:44.9506931Z         x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:44.9507175Z         
2025-03-14T04:57:44.9507563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:44.9508053Z         mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:44.9508345Z         y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:44.9508587Z         
2025-03-14T04:57:44.9509041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:44.9509643Z         pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:44.9509944Z         
2025-03-14T04:57:44.9510382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:44.9510955Z         predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320));  pred_boxes = None
2025-03-14T04:57:44.9511265Z         
2025-03-14T04:57:44.9511775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:57:44.9512423Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:57:44.9512798Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:57:44.9513109Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:57:44.9513418Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:57:44.9513745Z         getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:57:44.9514006Z         
2025-03-14T04:57:44.9514392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:57:44.9514960Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:57:44.9515309Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:57:44.9515558Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:57:44.9515929Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:57:44.9516291Z         getitem_26: "Sym(1231 - s0)" = size_3[0]
2025-03-14T04:57:44.9516541Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:57:44.9516761Z         
2025-03-14T04:57:44.9517188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:57:44.9517799Z         probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:57:44.9518129Z         
2025-03-14T04:57:44.9518575Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:57:44.9519182Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:57:44.9519565Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:57:44.9519867Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:57:44.9520177Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:57:44.9520500Z         getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:57:44.9520767Z         
2025-03-14T04:57:44.9521333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:57:44.9522045Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:57:44.9522405Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:44.9522756Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:57:44.9523107Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:57:44.9523412Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:57:44.9523656Z         
2025-03-14T04:57:44.9524108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:57:44.9524669Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:57:44.9524904Z         
2025-03-14T04:57:47.1373030Z 
2025-03-14T04:57:47.1373831Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:47.1374320Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T04:57:47.1375021Z         l_scores_0_ = L_scores_0_
2025-03-14T04:57:47.1375244Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:57:47.1375480Z         
2025-03-14T04:57:47.1376264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:57:47.1377046Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:57:47.1377426Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:47.1377796Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:57:47.1378154Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:57:47.1378480Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:57:47.1378742Z         
2025-03-14T04:57:47.1379242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:57:47.1379826Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:57:47.1380073Z         
2025-03-14T04:57:47.1380167Z 
2025-03-14T04:57:47.1380267Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:47.1380585Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T04:57:47.1380892Z         l_scores_0_ = L_scores_0_
2025-03-14T04:57:47.1381092Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:57:47.1381278Z         
2025-03-14T04:57:47.1381857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:57:47.1382553Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:57:47.1382978Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:47.1383282Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:57:47.1383585Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:57:47.1383869Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:57:47.1384100Z         
2025-03-14T04:57:47.1384526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:57:47.1385030Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:57:47.1385248Z         
2025-03-14T04:58:01.7729302Z Compilation time (from dynamo_timed): 33.036637935
2025-03-14T04:58:01.7740095Z pass
2025-03-14T04:58:01.7747772Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:58:01.7753459Z TIMING: entire_frame_compile:33.03664 gc:0.03164 _recursive_pre_grad_passes:0.02779 async_compile.wait:8.59046 backend_compile:22.47145 _recursive_joint_graph_passes:0.16712 _recursive_post_grad_passes:0.08565 code_gen:11.51381 inductor_compile:13.23381 total_wall_time:33.03664
2025-03-14T04:58:01.7754635Z STATS: call_* op count: 607 | FakeTensorMode.__torch_dispatch__:17714 | FakeTensor.__torch_dispatch__:1777 | ProxyTorchDispatchMode.__torch_dispatch__:5620 | attempt fast:51 | slow no contiguity match:20 | fast is_contiguous:31
2025-03-14T04:58:01.7755499Z Dynamo produced 53 graphs covering 607 ops with 42 graph breaks (6 unique)
2025-03-14T04:58:06.7835365Z 
2025-03-14T04:58:16.9164093Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:58:16.9164675Z loading model: 0it [00:10, ?it/s]
2025-03-14T04:58:16.9174383Z cpu  eval  detectron2_fasterrcnn_r_50_dc5     
2025-03-14T04:58:30.1764994Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_50_dc5. Setting accuracy check to cosine
2025-03-14T04:58:30.2126490Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:58:41.9599533Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:58:54.4093897Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:59:04.0985956Z 
2025-03-14T04:59:04.0991825Z class GraphModule(torch.nn.Module):
2025-03-14T04:59:04.1066909Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:59:04.1120559Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:59:04.1121073Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1121916Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1122729Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1123503Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1124233Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1124933Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1125703Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1126593Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1127503Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1128333Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1129138Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1130005Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1130904Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1131827Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1132619Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1133324Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1134064Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1134818Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1135534Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1136227Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1136941Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:59:04.1137702Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1138464Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1139210Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:59:04.1139923Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:59:04.1140593Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1141281Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1142025Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1142767Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1143473Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1144143Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1144831Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1145577Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1146292Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1146985Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1147647Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1148332Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1149072Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1149790Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1150484Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1151158Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1151833Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1152558Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1153268Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1153949Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1154594Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1155271Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1156023Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1156747Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1157469Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1158123Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1158803Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1159536Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1160260Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1161229Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1161893Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1162585Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1163341Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1164085Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1164819Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1165508Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1166219Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1166997Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1167752Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1168481Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1169189Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1169955Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1170782Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1171616Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1172424Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1173161Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:59:04.1173923Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1174722Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1175500Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:59:04.1176254Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:59:04.1176960Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1177681Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1178453Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1179209Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1179959Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1180769Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1181452Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1182180Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1182887Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1183575Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1184225Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1184917Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1185662Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1186408Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1187099Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1187748Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1188448Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1189235Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1189993Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1190702Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1191354Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1192034Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1192770Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1193498Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1194182Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1194837Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1195517Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1196256Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1196965Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1197651Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1198316Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1198998Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1199740Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1200494Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1201241Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1201952Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1202706Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1203500Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1204276Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1204993Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1205646Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1206361Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1207136Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1207900Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1208626Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1209322Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1210032Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1210809Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1211654Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1212431Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1213196Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1213914Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1214722Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1215482Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1216215Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1216910Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1217629Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1218407Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1219157Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1219882Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1220582Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:59:04.1221341Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1222163Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1222929Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:59:04.1223648Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:59:04.1224310Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1224991Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1225724Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1226431Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1227133Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1227786Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1228537Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1229266Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1229970Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1230655Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1231300Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1231975Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1232705Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1233411Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1234097Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1234768Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1235558Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1236300Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1237016Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1237711Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1238366Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1239054Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1239802Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1240541Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1241227Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1241894Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1242590Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1243322Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1244036Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1244716Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1245389Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1246095Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1246859Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1247582Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1248265Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1248918Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1249624Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1250351Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1251067Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1251849Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1252616Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1253378Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1254111Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1255247Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1256425Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1257381Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1258303Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1259130Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1259926Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1260905Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1261763Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1262700Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1263556Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1264285Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1264986Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1265693Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1266373Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1267103Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1267813Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1268514Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1269211Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1269973Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1270734Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1271454Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1272171Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1272858Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1273634Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1274375Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1275113Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1275802Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1276472Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1277330Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1278084Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1278794Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1279485Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1280181Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1280860Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1281613Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1282363Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1283138Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1283809Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1284503Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1285272Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1286170Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1287287Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1288216Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1289206Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1290075Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1290955Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1291878Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1292683Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:59:04.1293882Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1294844Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1295627Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:59:04.1296401Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:59:04.1297102Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1297823Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1298591Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1299344Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1300076Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1300767Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1301507Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1302280Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1303075Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1303800Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1304486Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1305197Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1305972Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1306730Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1307447Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1308129Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1308843Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1309613Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1310384Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1311102Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1311784Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1312504Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1313274Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1314023Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1314750Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1315439Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1316291Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1317082Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1317843Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1318546Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1319273Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:59:04.1320013Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:59:04.1320703Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:59:04.1321433Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:59:04.1322207Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:59:04.1322972Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:59:04.1323721Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:59:04.1324186Z         
2025-03-14T04:59:04.1324595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1325407Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1326024Z         
2025-03-14T04:59:04.1326402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1328265Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1329990Z         
2025-03-14T04:59:04.1330394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:59:04.1330883Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:59:04.1331153Z         
2025-03-14T04:59:04.1331724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:59:04.1332487Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:59:04.1332863Z         
2025-03-14T04:59:04.1333211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1333973Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1334525Z         
2025-03-14T04:59:04.1334881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1336810Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1338526Z         
2025-03-14T04:59:04.1338918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1339447Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:59:04.1339719Z         
2025-03-14T04:59:04.1340094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1340880Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1341455Z         
2025-03-14T04:59:04.1341828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1343818Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1345478Z         
2025-03-14T04:59:04.1345844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1346338Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:59:04.1346594Z         
2025-03-14T04:59:04.1346941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1347684Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1348230Z         
2025-03-14T04:59:04.1348579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1350432Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1352066Z         
2025-03-14T04:59:04.1352399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1353143Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:59:04.1353714Z         
2025-03-14T04:59:04.1354057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1355956Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1357660Z         
2025-03-14T04:59:04.1358020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1358497Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:59:04.1358756Z         
2025-03-14T04:59:04.1359141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1359653Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:59:04.1359947Z         
2025-03-14T04:59:04.1360301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1361266Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1361833Z         
2025-03-14T04:59:04.1362204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1364166Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1365917Z         
2025-03-14T04:59:04.1366311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1366823Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:59:04.1367099Z         
2025-03-14T04:59:04.1367447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1368260Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1368836Z         
2025-03-14T04:59:04.1369204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1371209Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1373169Z         
2025-03-14T04:59:04.1373585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1374084Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:59:04.1374343Z         
2025-03-14T04:59:04.1374669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1375460Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1376010Z         
2025-03-14T04:59:04.1376364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1378233Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1379877Z         
2025-03-14T04:59:04.1380249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1380747Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:59:04.1381021Z         
2025-03-14T04:59:04.1381396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1381893Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:59:04.1382174Z         
2025-03-14T04:59:04.1382509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1383237Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1383773Z         
2025-03-14T04:59:04.1384121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1385993Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1387645Z         
2025-03-14T04:59:04.1388008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1388500Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:59:04.1388759Z         
2025-03-14T04:59:04.1389107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1389837Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1390380Z         
2025-03-14T04:59:04.1390727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1392589Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1394219Z         
2025-03-14T04:59:04.1394586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1395060Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:59:04.1395319Z         
2025-03-14T04:59:04.1395650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1396405Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1396946Z         
2025-03-14T04:59:04.1397289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1399132Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1400763Z         
2025-03-14T04:59:04.1401136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1401623Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:59:04.1401892Z         
2025-03-14T04:59:04.1402274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1402778Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:59:04.1403050Z         
2025-03-14T04:59:04.1403382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1404118Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1404660Z         
2025-03-14T04:59:04.1405010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1406891Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1408561Z         
2025-03-14T04:59:04.1408931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1409419Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:59:04.1409699Z         
2025-03-14T04:59:04.1410038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1410789Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1411412Z         
2025-03-14T04:59:04.1411792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1413781Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1415556Z         
2025-03-14T04:59:04.1415951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1416500Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:59:04.1416784Z         
2025-03-14T04:59:04.1417156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1417951Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1418539Z         
2025-03-14T04:59:04.1418915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1420918Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1422564Z         
2025-03-14T04:59:04.1422896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1423637Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:59:04.1424210Z         
2025-03-14T04:59:04.1424557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1426476Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1428175Z         
2025-03-14T04:59:04.1428534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1429012Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:59:04.1429276Z         
2025-03-14T04:59:04.1429651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1430141Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:59:04.1430430Z         
2025-03-14T04:59:04.1430764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1431520Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1432061Z         
2025-03-14T04:59:04.1432406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1434279Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1435932Z         
2025-03-14T04:59:04.1436301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1436778Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:59:04.1437037Z         
2025-03-14T04:59:04.1437361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1438139Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1438689Z         
2025-03-14T04:59:04.1439036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1440897Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1442539Z         
2025-03-14T04:59:04.1442906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1443401Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:59:04.1443660Z         
2025-03-14T04:59:04.1443985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1444757Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1445306Z         
2025-03-14T04:59:04.1445650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1447574Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1449321Z         
2025-03-14T04:59:04.1449693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1450217Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:59:04.1450494Z         
2025-03-14T04:59:04.1450880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1451475Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:59:04.1451807Z         
2025-03-14T04:59:04.1452188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1453006Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1453545Z         
2025-03-14T04:59:04.1453916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1455884Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1457604Z         
2025-03-14T04:59:04.1458003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1458514Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:59:04.1458824Z         
2025-03-14T04:59:04.1459203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1460042Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1460708Z         
2025-03-14T04:59:04.1461080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1463038Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1464797Z         
2025-03-14T04:59:04.1465183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1465687Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:59:04.1465961Z         
2025-03-14T04:59:04.1466311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1467131Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1467709Z         
2025-03-14T04:59:04.1468076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1470039Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1471708Z         
2025-03-14T04:59:04.1472095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1472584Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:59:04.1472856Z         
2025-03-14T04:59:04.1473243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1473734Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:59:04.1474020Z         
2025-03-14T04:59:04.1474350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1475084Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1475619Z         
2025-03-14T04:59:04.1475960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1477829Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1479496Z         
2025-03-14T04:59:04.1479867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1480341Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:59:04.1480624Z         
2025-03-14T04:59:04.1480957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1481691Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1482237Z         
2025-03-14T04:59:04.1482581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1484515Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1486197Z         
2025-03-14T04:59:04.1486584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1487086Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:59:04.1487358Z         
2025-03-14T04:59:04.1487727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1488512Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1489093Z         
2025-03-14T04:59:04.1489466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1504466Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1506260Z         
2025-03-14T04:59:04.1506683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1507210Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:59:04.1507498Z         
2025-03-14T04:59:04.1507893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1508517Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:59:04.1508806Z         
2025-03-14T04:59:04.1509167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1509948Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1510510Z         
2025-03-14T04:59:04.1510880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1512881Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1514647Z         
2025-03-14T04:59:04.1515043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1516474Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:59:04.1516752Z         
2025-03-14T04:59:04.1517109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1517889Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1518464Z         
2025-03-14T04:59:04.1518836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1520780Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1522426Z         
2025-03-14T04:59:04.1522798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1523281Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:59:04.1523554Z         
2025-03-14T04:59:04.1523889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1524630Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1525170Z         
2025-03-14T04:59:04.1525520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1527480Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1529225Z         
2025-03-14T04:59:04.1529580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1530396Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:59:04.1530988Z         
2025-03-14T04:59:04.1531469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1533518Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1535224Z         
2025-03-14T04:59:04.1535590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1536071Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:59:04.1536369Z         
2025-03-14T04:59:04.1536877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1537494Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:59:04.1537755Z         
2025-03-14T04:59:04.1538117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1538837Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1539373Z         
2025-03-14T04:59:04.1539722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1541729Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1543545Z         
2025-03-14T04:59:04.1543937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1544416Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:59:04.1544688Z         
2025-03-14T04:59:04.1545022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1545772Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1546309Z         
2025-03-14T04:59:04.1546657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1548522Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1550171Z         
2025-03-14T04:59:04.1550546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1551016Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:59:04.1551378Z         
2025-03-14T04:59:04.1551727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1552461Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1553118Z         
2025-03-14T04:59:04.1553632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1555517Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1557432Z         
2025-03-14T04:59:04.1557823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1558370Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:59:04.1558649Z         
2025-03-14T04:59:04.1559039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1559573Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:59:04.1559845Z         
2025-03-14T04:59:04.1560269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1561364Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1561941Z         
2025-03-14T04:59:04.1562321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1564804Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1567424Z         
2025-03-14T04:59:04.1567996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1568763Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:59:04.1569136Z         
2025-03-14T04:59:04.1569667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1570922Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1571754Z         
2025-03-14T04:59:04.1572141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1574098Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1575783Z         
2025-03-14T04:59:04.1576180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1576661Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:59:04.1576940Z         
2025-03-14T04:59:04.1577277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1578034Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1578576Z         
2025-03-14T04:59:04.1578923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1580773Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1582415Z         
2025-03-14T04:59:04.1582775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1583253Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:59:04.1583511Z         
2025-03-14T04:59:04.1583872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1584380Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:59:04.1584643Z         
2025-03-14T04:59:04.1584975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1585699Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1586228Z         
2025-03-14T04:59:04.1586573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1588447Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1590091Z         
2025-03-14T04:59:04.1590459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1590948Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:59:04.1591201Z         
2025-03-14T04:59:04.1591538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1592277Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1592812Z         
2025-03-14T04:59:04.1593163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1595091Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1596818Z         
2025-03-14T04:59:04.1597212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1597716Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:59:04.1597981Z         
2025-03-14T04:59:04.1598333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1599115Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1599679Z         
2025-03-14T04:59:04.1600040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1601982Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1603690Z         
2025-03-14T04:59:04.1604075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1604577Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:59:04.1604867Z         
2025-03-14T04:59:04.1605247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1605768Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:59:04.1606038Z         
2025-03-14T04:59:04.1606386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1607140Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1607709Z         
2025-03-14T04:59:04.1608092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1610173Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1612068Z         
2025-03-14T04:59:04.1612480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1613010Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:59:04.1613309Z         
2025-03-14T04:59:04.1613671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1614490Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1615092Z         
2025-03-14T04:59:04.1615477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1617583Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1619418Z         
2025-03-14T04:59:04.1619827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1620370Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:59:04.1620650Z         
2025-03-14T04:59:04.1621034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1621853Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1622388Z         
2025-03-14T04:59:04.1622734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1624566Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1626176Z         
2025-03-14T04:59:04.1626531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1627007Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:59:04.1627276Z         
2025-03-14T04:59:04.1627657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1628175Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:59:04.1628446Z         
2025-03-14T04:59:04.1628779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1629495Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1630022Z         
2025-03-14T04:59:04.1630366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1632233Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1633878Z         
2025-03-14T04:59:04.1634246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1634735Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:59:04.1634987Z         
2025-03-14T04:59:04.1635334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1636105Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1636670Z         
2025-03-14T04:59:04.1637035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1639008Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1640734Z         
2025-03-14T04:59:04.1641114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1641609Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:59:04.1641891Z         
2025-03-14T04:59:04.1642232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1643010Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1643581Z         
2025-03-14T04:59:04.1643942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1645924Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1647626Z         
2025-03-14T04:59:04.1647990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1648491Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:59:04.1648770Z         
2025-03-14T04:59:04.1649168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1649663Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:59:04.1649934Z         
2025-03-14T04:59:04.1650280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1651029Z         x_88: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1651642Z         
2025-03-14T04:59:04.1652014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1654027Z         x_89: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1655684Z         
2025-03-14T04:59:04.1656069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1656586Z         out_52: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:59:04.1656839Z         
2025-03-14T04:59:04.1657168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1657897Z         x_90: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_52 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1658434Z         
2025-03-14T04:59:04.1658778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1660791Z         x_91: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1662459Z         
2025-03-14T04:59:04.1662828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1663323Z         out_53: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:59:04.1663577Z         
2025-03-14T04:59:04.1663906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1664638Z         x_92: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1665177Z         
2025-03-14T04:59:04.1665518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1667352Z         x_93: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1668982Z         
2025-03-14T04:59:04.1669312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1670059Z         x_94: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_51 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:59:04.1670644Z         
2025-03-14T04:59:04.1670988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1672988Z         x_95: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1674758Z         
2025-03-14T04:59:04.1675133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1675646Z         x_93 += x_95;  out_54: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_93;  x_93 = x_95 = None
2025-03-14T04:59:04.1675924Z         
2025-03-14T04:59:04.1676309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1676832Z         out_55: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:59:04.1677103Z         
2025-03-14T04:59:04.1677465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1678221Z         x_96: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1678778Z         
2025-03-14T04:59:04.1679143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1681094Z         x_97: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1682818Z         
2025-03-14T04:59:04.1683204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1683702Z         out_56: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:59:04.1683967Z         
2025-03-14T04:59:04.1684331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1685106Z         x_98: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_56 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1685667Z         
2025-03-14T04:59:04.1686032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1687993Z         x_99: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1689710Z         
2025-03-14T04:59:04.1690108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1690610Z         out_57: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_99);  x_99 = None
2025-03-14T04:59:04.1690893Z         
2025-03-14T04:59:04.1691251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1692179Z         x_100: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1692790Z         
2025-03-14T04:59:04.1693180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1695110Z         x_101: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1696764Z         
2025-03-14T04:59:04.1697128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1697614Z         x_101 += out_55;  out_58: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_101;  x_101 = out_55 = None
2025-03-14T04:59:04.1697896Z         
2025-03-14T04:59:04.1698268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1698766Z         out_59: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:59:04.1699029Z         
2025-03-14T04:59:04.1699359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1700083Z         x_102: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.1700612Z         
2025-03-14T04:59:04.1700955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1702817Z         x_103: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1704473Z         
2025-03-14T04:59:04.1704841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1705336Z         out_60: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:59:04.1705593Z         
2025-03-14T04:59:04.1705931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1706667Z         x_104: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_60 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.1707200Z         
2025-03-14T04:59:04.1707545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1709435Z         x_105: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1711141Z         
2025-03-14T04:59:04.1711526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1712027Z         out_61: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_105);  x_105 = None
2025-03-14T04:59:04.1712318Z         
2025-03-14T04:59:04.1712670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1713446Z         x_106: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.1714020Z         
2025-03-14T04:59:04.1714387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.1716338Z         x_107: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.1718056Z         
2025-03-14T04:59:04.1718448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.1718956Z         x_107 += out_59;  out_62: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_107;  x_107 = out_59 = None
2025-03-14T04:59:04.1719255Z         
2025-03-14T04:59:04.1719641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.1720167Z         out_63: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:59:04.1720441Z         
2025-03-14T04:59:04.1720975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:59:04.1721617Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:59:04.1721884Z         
2025-03-14T04:59:04.1722262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:04.1722748Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:59:04.1723002Z         
2025-03-14T04:59:04.1723524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:59:04.1724157Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:59:04.1724422Z         
2025-03-14T04:59:04.1724795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:04.1725279Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:59:04.1725534Z         
2025-03-14T04:59:04.1725989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:59:04.1726611Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:59:04.1726945Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:59:04.1727212Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:59:04.1727444Z         
2025-03-14T04:59:04.1727856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:59:04.1728373Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:59:04.1728628Z         
2025-03-14T04:59:04.1729064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:59:04.1729558Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:59:04.1729792Z         
2025-03-14T04:59:04.1730254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:59:04.1730896Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:59:04.1731227Z         
2025-03-14T04:59:04.1731842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:59:04.1732510Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:59:04.1733142Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:59:04.1733763Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:59:04.1734051Z         x_108: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:59:04.1734282Z         
2025-03-14T04:59:04.1734670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:59:04.1735146Z         tensor: "f32[82125, 4][4, 1]cpu" = x_108.to(torch.float32);  x_108 = None
2025-03-14T04:59:04.1735392Z         
2025-03-14T04:59:04.1735727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.1736808Z         x_109: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_63 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:59:04.1737688Z         
2025-03-14T04:59:04.1738042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:59:04.1738557Z         x_110: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_109, inplace = False);  x_109 = None
2025-03-14T04:59:04.1738860Z         
2025-03-14T04:59:04.1739320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:59:04.1740601Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:59:04.1741586Z         
2025-03-14T04:59:04.1742024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:59:04.1743241Z         x_111: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_110 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:59:04.1744166Z         
2025-03-14T04:59:04.1744582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:59:04.1745119Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:59:04.1745462Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:59:04.1745737Z         
2025-03-14T04:59:04.1746247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:59:04.1746887Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_111.view(4, -1, 4, 73, 75);  x_111 = None
2025-03-14T04:59:04.1747282Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:59:04.1747684Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:59:04.1747974Z         
2025-03-14T04:59:04.1748460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:59:04.1749117Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:59:04.1749433Z         
2025-03-14T04:59:04.1749952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:59:04.1750581Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:59:04.1750932Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:59:04.1751271Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:59:04.1751524Z         
2025-03-14T04:59:04.1751985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:59:04.1752578Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:59:04.1752857Z         
2025-03-14T04:59:04.1753257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:59:04.1753781Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:59:04.1754040Z         
2025-03-14T04:59:04.1754443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:59:04.1754940Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:59:04.1755252Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:04.1755581Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:59:04.1755850Z         
2025-03-14T04:59:04.1756256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:59:04.1756751Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:59:04.1757056Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:59:04.1757381Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:59:04.1757644Z         
2025-03-14T04:59:04.1758035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:59:04.1758510Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:04.1758809Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:59:04.1759066Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:59:04.1759299Z         
2025-03-14T04:59:04.1759688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:59:04.1760212Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:59:04.1760661Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:59:04.1761090Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:59:04.1761340Z         
2025-03-14T04:59:04.1761765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:59:04.1762268Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:59:04.1762604Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:59:04.1762834Z         
2025-03-14T04:59:04.1763222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:59:04.1763720Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:59:04.1764034Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:59:04.1764266Z         
2025-03-14T04:59:04.1764649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:59:04.1765151Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:59:04.1765466Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:59:04.1765700Z         
2025-03-14T04:59:04.1766090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:59:04.1766634Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:59:04.1767037Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:59:04.1767273Z         
2025-03-14T04:59:04.1767702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:59:04.1768245Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:59:04.1768511Z         
2025-03-14T04:59:04.1768935Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:59:04.1769465Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:59:04.1769722Z         
2025-03-14T04:59:04.1770158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:59:04.1770707Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:59:04.1771030Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:59:04.1771410Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:59:04.1771779Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:59:04.1772040Z         
2025-03-14T04:59:04.1772515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:59:04.1773081Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:59:04.1773438Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:59:04.1773765Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:59:04.1774133Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:59:04.1774388Z         
2025-03-14T04:59:04.1774803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:59:04.1775298Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:59:04.1775626Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:59:04.1775966Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:59:04.1776214Z         
2025-03-14T04:59:04.1776627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:59:04.1777124Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:59:04.1777451Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:59:04.1777802Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:59:04.1778051Z         
2025-03-14T04:59:04.1778440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:59:04.1778895Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:59:04.1779155Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:59:04.1779390Z         
2025-03-14T04:59:04.1779779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:59:04.1780251Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:59:04.1780510Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:59:04.1780743Z         
2025-03-14T04:59:04.1781127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:59:04.1781595Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:59:04.1781888Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:59:04.1782135Z         
2025-03-14T04:59:04.1782520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:59:04.1782988Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:59:04.1783273Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:59:04.1783516Z         
2025-03-14T04:59:04.1783953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:59:04.1784521Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:59:04.1784809Z         
2025-03-14T04:59:04.1785235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:59:04.1785780Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:59:04.1786073Z         
2025-03-14T04:59:04.1786534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:59:04.1787147Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:59:04.1787433Z         
2025-03-14T04:59:04.1788006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:59:04.1788682Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:59:04.1788933Z         
2025-03-14T04:59:04.1789313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:04.1789810Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:59:04.1790066Z         
2025-03-14T04:59:04.1790590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:59:04.1791193Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:59:04.1791461Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:59:04.1791727Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:59:04.1791957Z         
2025-03-14T04:59:04.1792506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:59:04.1793185Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:59:04.1793640Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:59:04.1794002Z         
2025-03-14T04:59:04.1794546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:59:04.1795224Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:59:04.1795503Z         
2025-03-14T04:59:04.1795879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:04.1796384Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:59:04.1796652Z         
2025-03-14T04:59:04.1797119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:59:04.1797693Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:59:04.1797950Z         
2025-03-14T04:59:04.1798330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:59:04.1798822Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:59:04.1799096Z         
2025-03-14T04:59:04.1799555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:59:04.1800135Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:59:04.1800386Z         
2025-03-14T04:59:04.1800965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:59:04.1801629Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:59:04.1801936Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:59:04.1802255Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:59:04.1802586Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:59:04.1802834Z         
2025-03-14T04:59:04.1803280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:59:04.1803813Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:59:04.1804045Z         
2025-03-14T04:59:04.1804444Z 
2025-03-14T04:59:04.1804541Z class GraphModule(torch.nn.Module):
2025-03-14T04:59:04.1858052Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:59:04.1909948Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:59:04.1910459Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1911246Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1912085Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1912844Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1913506Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1914216Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1914916Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1915734Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1916506Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1917250Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1917904Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1918602Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1919343Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1920063Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1920762Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1921413Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1922102Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1922848Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1923600Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1924295Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1924975Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:59:04.1925684Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1926450Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1927195Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:59:04.1927932Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:59:04.1928609Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1929317Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1930083Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1930814Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1931600Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1932344Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1933025Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1933363Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1933690Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1933997Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1934288Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1934625Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1935010Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1935341Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1935650Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1935947Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1936279Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1936613Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1936930Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1937263Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1937545Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1937929Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1938284Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1938610Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1938919Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1939198Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1939546Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1939877Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1940192Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1940494Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1940778Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1941142Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1941467Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1941784Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1942083Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1942367Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1942699Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1943031Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1943356Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1943669Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1943965Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1944318Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1944662Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1944986Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1945294Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1945587Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:59:04.1945942Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1946282Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1946616Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:59:04.1949438Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:59:04.1949733Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1950081Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1950423Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1950751Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1951098Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1951391Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1951734Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1952085Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1952424Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1952769Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1953080Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1953474Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1953865Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1954224Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1954551Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1954844Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1955229Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1955615Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1955987Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1956373Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1956666Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1957018Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1957362Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1957693Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1958008Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1958301Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1958644Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1959013Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1959377Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1959736Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1960029Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1960374Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1960941Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1961277Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1961600Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1961883Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1962230Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1962568Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1962954Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1963277Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1963564Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1963911Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1964250Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1964577Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1964886Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1965177Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1965519Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1965887Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1966238Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1966596Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1966899Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1967267Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1967631Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1967974Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1968322Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1968617Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1969007Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1969369Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1969734Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1970079Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1970413Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:59:04.1970799Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1971179Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1971609Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:59:04.1971972Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:59:04.1972279Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1972665Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1973023Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1973399Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1973727Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1974034Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1974392Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1974756Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1975098Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1975422Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1975725Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1976084Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1976470Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1976807Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1977140Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1977438Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1977806Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1978161Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1978503Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1978837Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1979151Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1979515Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1979888Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1980246Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1980562Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1980859Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1981204Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1981548Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1981877Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1982188Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1982484Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1982827Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1983190Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1983508Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1983827Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1984111Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1984459Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1984808Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1985125Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1985442Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1985768Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1986121Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1986489Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1986825Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1987135Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1987423Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1987762Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1988103Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1988432Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1988742Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1989032Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1989392Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1989736Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1990056Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1990374Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1990656Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1991009Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1991352Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1991677Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1992007Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1992292Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1992657Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1993009Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1993341Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1993662Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1993953Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1994303Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1994640Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1994966Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.1995278Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.1995587Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:59:04.1995928Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1996267Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1996591Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.1996898Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.1997203Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:59:04.1997573Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.1997914Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.1998232Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.1998580Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.1999603Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:59:04.1999990Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2000350Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2000671Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2000990Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2001276Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2001624Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2001967Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2002310Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2002637Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2002987Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:59:04.2003366Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2003738Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2004092Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:59:04.2004428Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:59:04.2004735Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2005094Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2005455Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2005807Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2006144Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2006484Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2006845Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2007208Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2007544Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2007875Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2008172Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2008546Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2008895Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2009238Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2009607Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2009906Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2010270Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2010619Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2010959Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2011287Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2011655Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2012026Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2012433Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2012795Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2013139Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2013460Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2013820Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2014184Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2014522Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2014864Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2015234Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:59:04.2015578Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:59:04.2015912Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:59:04.2016301Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:59:04.2016707Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:59:04.2017077Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:59:04.2017445Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:59:04.2017517Z         
2025-03-14T04:59:04.2017830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2018337Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2018416Z         
2025-03-14T04:59:04.2018712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2020273Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2020375Z         
2025-03-14T04:59:04.2020668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:59:04.2020818Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:59:04.2020880Z         
2025-03-14T04:59:04.2021259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:59:04.2021499Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:59:04.2021572Z         
2025-03-14T04:59:04.2021829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2022266Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2022328Z         
2025-03-14T04:59:04.2022603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2024175Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2024260Z         
2025-03-14T04:59:04.2024553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2024688Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:59:04.2024758Z         
2025-03-14T04:59:04.2025010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2025446Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2025506Z         
2025-03-14T04:59:04.2025779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2027353Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2027431Z         
2025-03-14T04:59:04.2027723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2027863Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:59:04.2027931Z         
2025-03-14T04:59:04.2028184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2028638Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2028706Z         
2025-03-14T04:59:04.2028969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2030536Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2030618Z         
2025-03-14T04:59:04.2030878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2031319Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:59:04.2031392Z         
2025-03-14T04:59:04.2031665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2033368Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2033456Z         
2025-03-14T04:59:04.2033733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2033911Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:59:04.2033973Z         
2025-03-14T04:59:04.2034263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2034411Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:59:04.2034481Z         
2025-03-14T04:59:04.2034730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2035166Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2035235Z         
2025-03-14T04:59:04.2035507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2037051Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2037135Z         
2025-03-14T04:59:04.2037430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2037579Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:59:04.2037640Z         
2025-03-14T04:59:04.2037895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2038326Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2038398Z         
2025-03-14T04:59:04.2038662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2040222Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2040309Z         
2025-03-14T04:59:04.2040610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2040757Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:59:04.2040817Z         
2025-03-14T04:59:04.2041075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2041515Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2041588Z         
2025-03-14T04:59:04.2041851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2043410Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2043496Z         
2025-03-14T04:59:04.2043773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2043935Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:59:04.2043995Z         
2025-03-14T04:59:04.2044281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2044427Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:59:04.2044493Z         
2025-03-14T04:59:04.2044741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2045174Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2045233Z         
2025-03-14T04:59:04.2045504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2047112Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2047205Z         
2025-03-14T04:59:04.2047516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2047662Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:59:04.2047734Z         
2025-03-14T04:59:04.2048010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2048481Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2048550Z         
2025-03-14T04:59:04.2048835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2050468Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2050561Z         
2025-03-14T04:59:04.2050871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2051017Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:59:04.2051091Z         
2025-03-14T04:59:04.2051444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2051978Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2052052Z         
2025-03-14T04:59:04.2052400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2054052Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2054154Z         
2025-03-14T04:59:04.2054456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2054619Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:59:04.2054692Z         
2025-03-14T04:59:04.2054990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2055157Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:59:04.2055223Z         
2025-03-14T04:59:04.2055495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2055953Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2056023Z         
2025-03-14T04:59:04.2056298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2057930Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2058023Z         
2025-03-14T04:59:04.2058323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2058482Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:59:04.2058544Z         
2025-03-14T04:59:04.2058817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2059283Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2059355Z         
2025-03-14T04:59:04.2059628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2061523Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2061627Z         
2025-03-14T04:59:04.2061931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2062086Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:59:04.2062153Z         
2025-03-14T04:59:04.2062428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2062864Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2062936Z         
2025-03-14T04:59:04.2063197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2064780Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2064898Z         
2025-03-14T04:59:04.2065149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2065600Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:59:04.2065668Z         
2025-03-14T04:59:04.2065945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2067563Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2067641Z         
2025-03-14T04:59:04.2067928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2068088Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:59:04.2068159Z         
2025-03-14T04:59:04.2068446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2068606Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:59:04.2068666Z         
2025-03-14T04:59:04.2068925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2069351Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2069424Z         
2025-03-14T04:59:04.2069690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2071284Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2071372Z         
2025-03-14T04:59:04.2071657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2071805Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:59:04.2071864Z         
2025-03-14T04:59:04.2072124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2072560Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2072629Z         
2025-03-14T04:59:04.2072904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2074473Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2074559Z         
2025-03-14T04:59:04.2074859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2075005Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:59:04.2075067Z         
2025-03-14T04:59:04.2075319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2075749Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2075822Z         
2025-03-14T04:59:04.2076091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2077627Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2077715Z         
2025-03-14T04:59:04.2077994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2078154Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:59:04.2078214Z         
2025-03-14T04:59:04.2078503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2078649Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:59:04.2078718Z         
2025-03-14T04:59:04.2078963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2079395Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2079464Z         
2025-03-14T04:59:04.2079726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2081306Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2081383Z         
2025-03-14T04:59:04.2081673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2081818Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:59:04.2081879Z         
2025-03-14T04:59:04.2082136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2082569Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2082637Z         
2025-03-14T04:59:04.2082892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2084392Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2084478Z         
2025-03-14T04:59:04.2084763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2084907Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:59:04.2084966Z         
2025-03-14T04:59:04.2085220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2085655Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2085724Z         
2025-03-14T04:59:04.2085989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2087549Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2087633Z         
2025-03-14T04:59:04.2087926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2088094Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:59:04.2088154Z         
2025-03-14T04:59:04.2088444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2088592Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:59:04.2088661Z         
2025-03-14T04:59:04.2088910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2089340Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2089400Z         
2025-03-14T04:59:04.2089672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2091231Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2091318Z         
2025-03-14T04:59:04.2091656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2091800Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:59:04.2091868Z         
2025-03-14T04:59:04.2092115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2092579Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2092642Z         
2025-03-14T04:59:04.2092926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2094520Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2094607Z         
2025-03-14T04:59:04.2094904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2095044Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:59:04.2095113Z         
2025-03-14T04:59:04.2095362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2095808Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2095871Z         
2025-03-14T04:59:04.2096144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2097700Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2097781Z         
2025-03-14T04:59:04.2098065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2098218Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:59:04.2098286Z         
2025-03-14T04:59:04.2098569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2098725Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:59:04.2098786Z         
2025-03-14T04:59:04.2099046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2099480Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2099551Z         
2025-03-14T04:59:04.2099815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2101486Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2101572Z         
2025-03-14T04:59:04.2101856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2101997Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:59:04.2102060Z         
2025-03-14T04:59:04.2102319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2102748Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2102817Z         
2025-03-14T04:59:04.2103078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2104626Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2104710Z         
2025-03-14T04:59:04.2104992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2105133Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:59:04.2105194Z         
2025-03-14T04:59:04.2105446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2105878Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2105947Z         
2025-03-14T04:59:04.2106214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2107801Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2107888Z         
2025-03-14T04:59:04.2108138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2108576Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:59:04.2108640Z         
2025-03-14T04:59:04.2108907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2110503Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2110588Z         
2025-03-14T04:59:04.2110874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2111013Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:59:04.2111081Z         
2025-03-14T04:59:04.2111361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2111511Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:59:04.2111569Z         
2025-03-14T04:59:04.2111831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2112254Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2112321Z         
2025-03-14T04:59:04.2112590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2114186Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2114272Z         
2025-03-14T04:59:04.2114555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2114694Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:59:04.2114755Z         
2025-03-14T04:59:04.2115007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2115428Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2115498Z         
2025-03-14T04:59:04.2115765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2117294Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2117384Z         
2025-03-14T04:59:04.2117670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2117812Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:59:04.2117876Z         
2025-03-14T04:59:04.2118134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2118566Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2118641Z         
2025-03-14T04:59:04.2118916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2120482Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2120571Z         
2025-03-14T04:59:04.2120862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2121021Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:59:04.2121086Z         
2025-03-14T04:59:04.2121379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2121519Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:59:04.2121589Z         
2025-03-14T04:59:04.2121842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2122268Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2122337Z         
2025-03-14T04:59:04.2122603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2124152Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2124233Z         
2025-03-14T04:59:04.2124522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2124658Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:59:04.2124719Z         
2025-03-14T04:59:04.2124973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2125389Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2125459Z         
2025-03-14T04:59:04.2125718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2127275Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2127364Z         
2025-03-14T04:59:04.2127650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2127788Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:59:04.2127847Z         
2025-03-14T04:59:04.2128100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2128519Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2128587Z         
2025-03-14T04:59:04.2128844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2130387Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2130473Z         
2025-03-14T04:59:04.2130750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2130902Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:59:04.2130963Z         
2025-03-14T04:59:04.2131273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2131543Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:59:04.2131627Z         
2025-03-14T04:59:04.2131903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2132372Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2132439Z         
2025-03-14T04:59:04.2132735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2134442Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2134551Z         
2025-03-14T04:59:04.2134878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2135024Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:59:04.2135099Z         
2025-03-14T04:59:04.2135591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2136085Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2136154Z         
2025-03-14T04:59:04.2136455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2138172Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2138315Z         
2025-03-14T04:59:04.2138602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2138730Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:59:04.2138800Z         
2025-03-14T04:59:04.2139045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2139468Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2139529Z         
2025-03-14T04:59:04.2139793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2141319Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2141403Z         
2025-03-14T04:59:04.2141683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2141822Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:59:04.2141887Z         
2025-03-14T04:59:04.2142159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2142298Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:59:04.2142357Z         
2025-03-14T04:59:04.2142606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2143002Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2143068Z         
2025-03-14T04:59:04.2143323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2144803Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2144890Z         
2025-03-14T04:59:04.2145163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2145296Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:59:04.2145356Z         
2025-03-14T04:59:04.2145606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2146014Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2146081Z         
2025-03-14T04:59:04.2146333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2147835Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2147923Z         
2025-03-14T04:59:04.2148196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2148331Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:59:04.2148391Z         
2025-03-14T04:59:04.2148642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2149056Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2149123Z         
2025-03-14T04:59:04.2149376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2150864Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2150945Z         
2025-03-14T04:59:04.2151213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2151361Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:59:04.2151421Z         
2025-03-14T04:59:04.2151699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2151832Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:59:04.2151901Z         
2025-03-14T04:59:04.2152138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2152549Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2152609Z         
2025-03-14T04:59:04.2152868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2154387Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2154462Z         
2025-03-14T04:59:04.2154746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2154871Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:59:04.2154941Z         
2025-03-14T04:59:04.2155185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2155600Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2155659Z         
2025-03-14T04:59:04.2155926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2157420Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2157500Z         
2025-03-14T04:59:04.2157782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2157911Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:59:04.2157978Z         
2025-03-14T04:59:04.2158218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2158641Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2158700Z         
2025-03-14T04:59:04.2158963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2160497Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2160712Z         
2025-03-14T04:59:04.2160999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2161142Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:59:04.2161210Z         
2025-03-14T04:59:04.2161482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2161628Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:59:04.2161686Z         
2025-03-14T04:59:04.2161936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2162340Z         x_88: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2162407Z         
2025-03-14T04:59:04.2162669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2164164Z         x_89: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2164267Z         
2025-03-14T04:59:04.2164543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2164679Z         out_52: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:59:04.2164738Z         
2025-03-14T04:59:04.2164983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2165396Z         x_90: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_52 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2165456Z         
2025-03-14T04:59:04.2165717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2167275Z         x_91: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2167364Z         
2025-03-14T04:59:04.2167652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2167787Z         out_53: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:59:04.2167848Z         
2025-03-14T04:59:04.2168105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2168542Z         x_92: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2168604Z         
2025-03-14T04:59:04.2168870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2170413Z         x_93: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2170504Z         
2025-03-14T04:59:04.2170757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2171206Z         x_94: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_51 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:59:04.2171278Z         
2025-03-14T04:59:04.2171668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2173403Z         x_95: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2173479Z         
2025-03-14T04:59:04.2173783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2173929Z         x_93 += x_95;  out_54: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_93;  x_93 = x_95 = None
2025-03-14T04:59:04.2173991Z         
2025-03-14T04:59:04.2174280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2174422Z         out_55: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:59:04.2174494Z         
2025-03-14T04:59:04.2174741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2175165Z         x_96: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2175227Z         
2025-03-14T04:59:04.2175496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2177029Z         x_97: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2177113Z         
2025-03-14T04:59:04.2177407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2177539Z         out_56: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:59:04.2177609Z         
2025-03-14T04:59:04.2177862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2178291Z         x_98: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_56 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2178356Z         
2025-03-14T04:59:04.2178629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2180199Z         x_99: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2180282Z         
2025-03-14T04:59:04.2180567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2180692Z         out_57: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_99);  x_99 = None
2025-03-14T04:59:04.2180756Z         
2025-03-14T04:59:04.2181007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2181441Z         x_100: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2181514Z         
2025-03-14T04:59:04.2181782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2183316Z         x_101: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2183406Z         
2025-03-14T04:59:04.2183693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2183844Z         x_101 += out_55;  out_58: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_101;  x_101 = out_55 = None
2025-03-14T04:59:04.2183911Z         
2025-03-14T04:59:04.2184189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2184337Z         out_59: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:59:04.2184397Z         
2025-03-14T04:59:04.2184652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2185069Z         x_102: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2185137Z         
2025-03-14T04:59:04.2185397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2186965Z         x_103: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2187047Z         
2025-03-14T04:59:04.2187333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2187477Z         out_60: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:59:04.2187537Z         
2025-03-14T04:59:04.2187791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2188217Z         x_104: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_60 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2188285Z         
2025-03-14T04:59:04.2188545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2190082Z         x_105: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2190168Z         
2025-03-14T04:59:04.2190450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2190592Z         out_61: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_105);  x_105 = None
2025-03-14T04:59:04.2190655Z         
2025-03-14T04:59:04.2190908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2191341Z         x_106: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2191411Z         
2025-03-14T04:59:04.2191676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2193236Z         x_107: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2193321Z         
2025-03-14T04:59:04.2193598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2193756Z         x_107 += out_59;  out_62: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_107;  x_107 = out_59 = None
2025-03-14T04:59:04.2193815Z         
2025-03-14T04:59:04.2194100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2194242Z         out_63: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:59:04.2194309Z         
2025-03-14T04:59:04.2194760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:59:04.2194919Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:59:04.2194979Z         
2025-03-14T04:59:04.2195275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:04.2195409Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:59:04.2195477Z         
2025-03-14T04:59:04.2195910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:59:04.2196081Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:59:04.2196141Z         
2025-03-14T04:59:04.2196437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:04.2196570Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:59:04.2196638Z         
2025-03-14T04:59:04.2197007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:59:04.2197188Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:59:04.2197282Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:59:04.2197407Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:59:04.2197468Z         
2025-03-14T04:59:04.2197807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:59:04.2197935Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:59:04.2197994Z         
2025-03-14T04:59:04.2198320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:59:04.2198437Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:59:04.2198518Z         
2025-03-14T04:59:04.2198897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:59:04.2199132Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:59:04.2199194Z         
2025-03-14T04:59:04.2199628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:59:04.2199751Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:59:04.2200193Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:59:04.2200314Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:59:04.2200437Z         x_108: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:59:04.2200499Z         
2025-03-14T04:59:04.2200803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:59:04.2200928Z         tensor: "f32[82125, 4][4, 1]cpu" = x_108.to(torch.float32);  x_108 = None
2025-03-14T04:59:04.2200996Z         
2025-03-14T04:59:04.2201245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2202034Z         x_109: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_63 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:59:04.2202110Z         
2025-03-14T04:59:04.2202388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:59:04.2202579Z         x_110: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_109, inplace = False);  x_109 = None
2025-03-14T04:59:04.2202647Z         
2025-03-14T04:59:04.2203028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:59:04.2203873Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:59:04.2203946Z         
2025-03-14T04:59:04.2204301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:59:04.2205143Z         x_111: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_110 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:59:04.2205209Z         
2025-03-14T04:59:04.2205566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:59:04.2205750Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:59:04.2205892Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:59:04.2205958Z         
2025-03-14T04:59:04.2206369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:59:04.2206535Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_111.view(4, -1, 4, 73, 75);  x_111 = None
2025-03-14T04:59:04.2206706Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:59:04.2206891Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:59:04.2206952Z         
2025-03-14T04:59:04.2207355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:59:04.2207559Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:59:04.2207629Z         
2025-03-14T04:59:04.2208061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:59:04.2208213Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:59:04.2208383Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:59:04.2208530Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:59:04.2208593Z         
2025-03-14T04:59:04.2208973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:59:04.2209139Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:59:04.2209209Z         
2025-03-14T04:59:04.2209520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:59:04.2209670Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:59:04.2209732Z         
2025-03-14T04:59:04.2210052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:59:04.2210184Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:59:04.2210315Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:04.2210458Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:59:04.2210531Z         
2025-03-14T04:59:04.2210859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:59:04.2211011Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:59:04.2211135Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:59:04.2211298Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:59:04.2211491Z         
2025-03-14T04:59:04.2211833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:59:04.2211978Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:04.2212079Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:59:04.2212204Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:59:04.2212278Z         
2025-03-14T04:59:04.2212607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:59:04.2212777Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:59:04.2212866Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:59:04.2213000Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:59:04.2213063Z         
2025-03-14T04:59:04.2213405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:59:04.2213562Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:59:04.2213673Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:59:04.2213742Z         
2025-03-14T04:59:04.2214042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:59:04.2214198Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:59:04.2214303Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:59:04.2214389Z         
2025-03-14T04:59:04.2214689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:59:04.2214851Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:59:04.2214960Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:59:04.2215029Z         
2025-03-14T04:59:04.2215331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:59:04.2215521Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:59:04.2215632Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:59:04.2215699Z         
2025-03-14T04:59:04.2216035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:59:04.2216182Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:59:04.2216243Z         
2025-03-14T04:59:04.2216578Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:59:04.2216711Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:59:04.2216779Z         
2025-03-14T04:59:04.2217138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:59:04.2217284Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:59:04.2217422Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:59:04.2217578Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:59:04.2217726Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:59:04.2217796Z         
2025-03-14T04:59:04.2218149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:59:04.2218291Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:59:04.2218408Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:59:04.2218565Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:59:04.2218699Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:59:04.2218768Z         
2025-03-14T04:59:04.2219104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:59:04.2219227Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:59:04.2219392Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:59:04.2219520Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:59:04.2219587Z         
2025-03-14T04:59:04.2219924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:59:04.2220043Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:59:04.2220218Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:59:04.2220352Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:59:04.2220414Z         
2025-03-14T04:59:04.2220726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:59:04.2220819Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:59:04.2220939Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:59:04.2221000Z         
2025-03-14T04:59:04.2221312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:59:04.2221402Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:59:04.2221521Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:59:04.2221584Z         
2025-03-14T04:59:04.2221892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:59:04.2222004Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:59:04.2222135Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:59:04.2222197Z         
2025-03-14T04:59:04.2222499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:59:04.2222607Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:59:04.2222749Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:59:04.2222812Z         
2025-03-14T04:59:04.2223175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:59:04.2223383Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:59:04.2223453Z         
2025-03-14T04:59:04.2223781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:59:04.2223949Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:59:04.2224009Z         
2025-03-14T04:59:04.2224400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:59:04.2224571Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:59:04.2224642Z         
2025-03-14T04:59:04.2225130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:59:04.2225262Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:59:04.2225321Z         
2025-03-14T04:59:04.2225610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:04.2225742Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:59:04.2225808Z         
2025-03-14T04:59:04.2226227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:59:04.2226363Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:59:04.2226460Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:59:04.2226574Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:59:04.2226633Z         
2025-03-14T04:59:04.2227083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:59:04.2227243Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:59:04.2227466Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:59:04.2227533Z         
2025-03-14T04:59:04.2227976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:59:04.2228142Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:59:04.2228200Z         
2025-03-14T04:59:04.2228488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:04.2228628Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:59:04.2228693Z         
2025-03-14T04:59:04.2229074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:59:04.2229223Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:59:04.2229297Z         
2025-03-14T04:59:04.2229616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:59:04.2229760Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:59:04.2229825Z         
2025-03-14T04:59:04.2230209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:59:04.2230349Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:59:04.2230411Z         
2025-03-14T04:59:04.2230904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:59:04.2231039Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:59:04.2231171Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:59:04.2231317Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:59:04.2231450Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:59:04.2231509Z         
2025-03-14T04:59:04.2231888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:59:04.2232000Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:59:04.2232069Z         
2025-03-14T04:59:04.2232095Z 
2025-03-14T04:59:04.2232188Z class GraphModule(torch.nn.Module):
2025-03-14T04:59:04.2284069Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:59:04.2284500Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:59:04.2284853Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2285239Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2285641Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2286015Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2286417Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2286788Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2287191Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2287611Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2288018Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2288380Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2288738Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2289162Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2289576Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2289982Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2290351Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2290707Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2291107Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2291569Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2291967Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2292333Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2292678Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:59:04.2293144Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2293561Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2293993Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:59:04.2294411Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:59:04.2294780Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2295197Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2295615Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2296015Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2296394Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2296771Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2297196Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2297614Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2298031Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2298409Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2298768Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2299188Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2299603Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2300014Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2300384Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2300747Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2301181Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2301601Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2302029Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2302401Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2302748Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2303129Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2303504Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2303858Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2304213Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2304526Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2304915Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2305310Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2305663Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2306022Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2306331Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2306718Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2307085Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2307442Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2307794Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2308117Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2308514Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2308897Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2309274Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2309625Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2309940Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2310313Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2310691Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2311043Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2311384Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2311729Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:59:04.2312111Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2312531Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2312903Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:59:04.2313260Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:59:04.2313573Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2313947Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2314319Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2314666Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2315045Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2315371Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2315773Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2316162Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2316521Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2316886Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2317199Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2317584Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2317964Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2318319Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2318668Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2318982Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2319372Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2319748Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2320104Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2320449Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2320760Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2321130Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2321500Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2321846Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2322207Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2322518Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2322928Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2323298Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2323646Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2323989Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2324298Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2324677Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2325040Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2325390Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2325723Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2326057Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2326433Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2326798Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2327153Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2327487Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2327808Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2328179Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2328550Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2328910Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2329256Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2331543Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2331928Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2332302Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2332668Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2333031Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2333365Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2333720Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2334073Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2334413Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2334736Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2335058Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2335499Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2336030Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2336353Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2336669Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2336969Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:59:04.2337329Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2337680Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2338042Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:59:04.2338377Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:59:04.2338751Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2339102Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2339439Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2339768Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2340081Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2340375Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2340723Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2341058Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2341386Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2341711Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2342000Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2342336Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2342678Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2342992Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2343310Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2343598Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2343935Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2344290Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2344609Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2344964Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2345249Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2345596Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2345933Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2346260Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2346582Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2346868Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2347221Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2347561Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2347925Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2348238Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2348530Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2348881Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2349233Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2349551Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2349871Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2350154Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2350513Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2350877Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2351251Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2351595Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2351880Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2352251Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2352589Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2352921Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2353244Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2353549Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2353891Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2354218Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2354558Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2354857Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2355139Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2355469Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2355800Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2356114Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2356420Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2356702Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2357045Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2357381Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2357722Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2358033Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2358310Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2358648Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2358975Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2359298Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2359608Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2359881Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2360224Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2360744Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2361182Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2361494Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2361784Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2362128Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2362471Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2362797Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2363107Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2363398Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2363771Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2364176Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2364501Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2364824Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2365117Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2365469Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2365819Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2366142Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2366462Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2366749Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2367103Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2367483Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2367825Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2368160Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2368486Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:59:04.2368865Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2369252Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2369632Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:59:04.2369973Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:59:04.2370290Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2370674Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2371079Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2371520Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2371891Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2372214Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2372661Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2373032Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2373378Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2373721Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2374018Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2374422Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2374779Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2375142Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2375489Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2375796Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:59:04.2376180Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2376532Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2376875Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:59:04.2377216Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:59:04.2377525Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:59:04.2377917Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2378282Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2378624Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:59:04.2378952Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:59:04.2379253Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:59:04.2379613Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:59:04.2379973Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:59:04.2380306Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:59:04.2380656Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:59:04.2381002Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:59:04.2381354Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:59:04.2381673Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:59:04.2382044Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:59:04.2382423Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:59:04.2382774Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:59:04.2383129Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:59:04.2383195Z         
2025-03-14T04:59:04.2383484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2383980Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2384056Z         
2025-03-14T04:59:04.2384332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2385881Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2385953Z         
2025-03-14T04:59:04.2386237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:59:04.2386385Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:59:04.2386448Z         
2025-03-14T04:59:04.2386812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:59:04.2387047Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:59:04.2387117Z         
2025-03-14T04:59:04.2387371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2387807Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2387907Z         
2025-03-14T04:59:04.2388172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2389751Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2389815Z         
2025-03-14T04:59:04.2390111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2390245Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:59:04.2390314Z         
2025-03-14T04:59:04.2390562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2391015Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2391097Z         
2025-03-14T04:59:04.2391377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2392932Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2392996Z         
2025-03-14T04:59:04.2393296Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2393434Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:59:04.2393502Z         
2025-03-14T04:59:04.2393757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2394210Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2394296Z         
2025-03-14T04:59:04.2394562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2396085Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2396151Z         
2025-03-14T04:59:04.2396407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2396848Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:59:04.2396911Z         
2025-03-14T04:59:04.2397191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2398856Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2398944Z         
2025-03-14T04:59:04.2399230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2399378Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:59:04.2399451Z         
2025-03-14T04:59:04.2399738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2399897Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:59:04.2399960Z         
2025-03-14T04:59:04.2400215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2400638Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2400707Z         
2025-03-14T04:59:04.2400983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2402527Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2402600Z         
2025-03-14T04:59:04.2402885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2403034Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:59:04.2403097Z         
2025-03-14T04:59:04.2403351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2403786Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2403856Z         
2025-03-14T04:59:04.2404115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2405704Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2405775Z         
2025-03-14T04:59:04.2406062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2406212Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:59:04.2406272Z         
2025-03-14T04:59:04.2406526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2406955Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2407026Z         
2025-03-14T04:59:04.2407288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2408888Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2408964Z         
2025-03-14T04:59:04.2409253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2409419Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:59:04.2409485Z         
2025-03-14T04:59:04.2409790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2409944Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:59:04.2410014Z         
2025-03-14T04:59:04.2410284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2410758Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2410843Z         
2025-03-14T04:59:04.2411130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2412857Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2412939Z         
2025-03-14T04:59:04.2413249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2413394Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:59:04.2413466Z         
2025-03-14T04:59:04.2413736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2414201Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2414265Z         
2025-03-14T04:59:04.2414573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2416209Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2416278Z         
2025-03-14T04:59:04.2416593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2416737Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:59:04.2416806Z         
2025-03-14T04:59:04.2417073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2417562Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2417627Z         
2025-03-14T04:59:04.2417923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2419626Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2419691Z         
2025-03-14T04:59:04.2419992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2420158Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:59:04.2420232Z         
2025-03-14T04:59:04.2420529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2420699Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:59:04.2420763Z         
2025-03-14T04:59:04.2421035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2421492Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2421580Z         
2025-03-14T04:59:04.2421850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2423381Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2423455Z         
2025-03-14T04:59:04.2423735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2423881Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:59:04.2423942Z         
2025-03-14T04:59:04.2424194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2424635Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2424720Z         
2025-03-14T04:59:04.2425002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2426557Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2426632Z         
2025-03-14T04:59:04.2426913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2427062Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:59:04.2427124Z         
2025-03-14T04:59:04.2427377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2427811Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2427900Z         
2025-03-14T04:59:04.2428165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2429720Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2429789Z         
2025-03-14T04:59:04.2430033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2430471Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:59:04.2430531Z         
2025-03-14T04:59:04.2430792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2432397Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2432476Z         
2025-03-14T04:59:04.2432761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2432907Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:59:04.2432978Z         
2025-03-14T04:59:04.2433263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2433422Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:59:04.2433484Z         
2025-03-14T04:59:04.2433750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2434176Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2434238Z         
2025-03-14T04:59:04.2434501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2436041Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2436110Z         
2025-03-14T04:59:04.2436382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2436527Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:59:04.2436586Z         
2025-03-14T04:59:04.2436836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2437259Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2437333Z         
2025-03-14T04:59:04.2437596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2439133Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2439223Z         
2025-03-14T04:59:04.2439498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2439643Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:59:04.2439714Z         
2025-03-14T04:59:04.2439954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2440382Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2440443Z         
2025-03-14T04:59:04.2440707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2442208Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2442290Z         
2025-03-14T04:59:04.2442572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2442722Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:59:04.2442790Z         
2025-03-14T04:59:04.2443070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2443226Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:59:04.2443286Z         
2025-03-14T04:59:04.2443537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2443968Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2444036Z         
2025-03-14T04:59:04.2444312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2445885Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2445957Z         
2025-03-14T04:59:04.2446237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2446379Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:59:04.2446438Z         
2025-03-14T04:59:04.2446687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2447107Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2447175Z         
2025-03-14T04:59:04.2447447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2448983Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2449050Z         
2025-03-14T04:59:04.2449331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2449475Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:59:04.2449535Z         
2025-03-14T04:59:04.2449793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2450269Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2450340Z         
2025-03-14T04:59:04.2450630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2452422Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2452495Z         
2025-03-14T04:59:04.2452777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2452945Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:59:04.2453008Z         
2025-03-14T04:59:04.2453298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2453446Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:59:04.2453515Z         
2025-03-14T04:59:04.2453774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2454217Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2454294Z         
2025-03-14T04:59:04.2454561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2456086Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2456151Z         
2025-03-14T04:59:04.2456447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2456581Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:59:04.2456652Z         
2025-03-14T04:59:04.2456895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2457366Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2457445Z         
2025-03-14T04:59:04.2457713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2459280Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2459345Z         
2025-03-14T04:59:04.2459637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2459774Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:59:04.2459840Z         
2025-03-14T04:59:04.2460089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2460661Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2460771Z         
2025-03-14T04:59:04.2461046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2462601Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2462666Z         
2025-03-14T04:59:04.2462952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2463102Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:59:04.2463171Z         
2025-03-14T04:59:04.2463448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2463600Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:59:04.2463660Z         
2025-03-14T04:59:04.2463936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2464357Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2464471Z         
2025-03-14T04:59:04.2464730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2466270Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2466342Z         
2025-03-14T04:59:04.2466623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2466763Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:59:04.2466823Z         
2025-03-14T04:59:04.2467076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2467495Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2467578Z         
2025-03-14T04:59:04.2467845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2469326Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2469395Z         
2025-03-14T04:59:04.2469669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2469805Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:59:04.2469864Z         
2025-03-14T04:59:04.2470112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2470539Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2470621Z         
2025-03-14T04:59:04.2470896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2472394Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2472464Z         
2025-03-14T04:59:04.2472713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2473153Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:59:04.2473214Z         
2025-03-14T04:59:04.2473482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2475070Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2475150Z         
2025-03-14T04:59:04.2475436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2475574Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:59:04.2475644Z         
2025-03-14T04:59:04.2475927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2476077Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:59:04.2476137Z         
2025-03-14T04:59:04.2476391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2476822Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2476885Z         
2025-03-14T04:59:04.2477160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2478724Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2478793Z         
2025-03-14T04:59:04.2479068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2479204Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:59:04.2479263Z         
2025-03-14T04:59:04.2479510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2479929Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2479989Z         
2025-03-14T04:59:04.2480258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2481810Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2481880Z         
2025-03-14T04:59:04.2482164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2482304Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:59:04.2482364Z         
2025-03-14T04:59:04.2482617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2483047Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2483107Z         
2025-03-14T04:59:04.2483391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2484968Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2485052Z         
2025-03-14T04:59:04.2485334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2485482Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:59:04.2485550Z         
2025-03-14T04:59:04.2485835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2485980Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:59:04.2486042Z         
2025-03-14T04:59:04.2486298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2486715Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2486798Z         
2025-03-14T04:59:04.2487061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2488669Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2488743Z         
2025-03-14T04:59:04.2489085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2489656Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:59:04.2489929Z         
2025-03-14T04:59:04.2490314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2491185Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2491893Z         
2025-03-14T04:59:04.2492305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2494227Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2495874Z         
2025-03-14T04:59:04.2496259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2496731Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:59:04.2496981Z         
2025-03-14T04:59:04.2497305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2498079Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2498618Z         
2025-03-14T04:59:04.2498958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2500858Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2502499Z         
2025-03-14T04:59:04.2502862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2503344Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:59:04.2503608Z         
2025-03-14T04:59:04.2503969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2504450Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:59:04.2504706Z         
2025-03-14T04:59:04.2505034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2505772Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2506319Z         
2025-03-14T04:59:04.2506678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2508559Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2510183Z         
2025-03-14T04:59:04.2510551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2511021Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:59:04.2511277Z         
2025-03-14T04:59:04.2511610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2512341Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2512897Z         
2025-03-14T04:59:04.2513244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2515179Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2516908Z         
2025-03-14T04:59:04.2517290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2517808Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:59:04.2518077Z         
2025-03-14T04:59:04.2518439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2519250Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2519834Z         
2025-03-14T04:59:04.2520213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2522240Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2523968Z         
2025-03-14T04:59:04.2524349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2524853Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:59:04.2525129Z         
2025-03-14T04:59:04.2525511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2526016Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:59:04.2526288Z         
2025-03-14T04:59:04.2526634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2527387Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2527951Z         
2025-03-14T04:59:04.2528295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2530238Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2532041Z         
2025-03-14T04:59:04.2532465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2533002Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:59:04.2533254Z         
2025-03-14T04:59:04.2533586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2534334Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2534887Z         
2025-03-14T04:59:04.2535248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2537135Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2538773Z         
2025-03-14T04:59:04.2539147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2539640Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:59:04.2539904Z         
2025-03-14T04:59:04.2540256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2541036Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2541609Z         
2025-03-14T04:59:04.2541956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2543810Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2545456Z         
2025-03-14T04:59:04.2545819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2546296Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:59:04.2546554Z         
2025-03-14T04:59:04.2546915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2547401Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:59:04.2547655Z         
2025-03-14T04:59:04.2548011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2548756Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2549315Z         
2025-03-14T04:59:04.2549659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2551519Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2553150Z         
2025-03-14T04:59:04.2553521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2553986Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:59:04.2554236Z         
2025-03-14T04:59:04.2554567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2555297Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2556176Z         
2025-03-14T04:59:04.2556530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2558415Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2560067Z         
2025-03-14T04:59:04.2560434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2561050Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:59:04.2561309Z         
2025-03-14T04:59:04.2561639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2562420Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2562983Z         
2025-03-14T04:59:04.2563347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2565192Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2566823Z         
2025-03-14T04:59:04.2567188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2567673Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:59:04.2567959Z         
2025-03-14T04:59:04.2568364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2568892Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:59:04.2569183Z         
2025-03-14T04:59:04.2569542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2570388Z         x_88: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2570985Z         
2025-03-14T04:59:04.2571396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2573429Z         x_89: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2575162Z         
2025-03-14T04:59:04.2575561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2576085Z         out_52: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:59:04.2576354Z         
2025-03-14T04:59:04.2576732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2577560Z         x_90: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_52 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2578155Z         
2025-03-14T04:59:04.2578527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2580491Z         x_91: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2582240Z         
2025-03-14T04:59:04.2582636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2583141Z         out_53: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:59:04.2583411Z         
2025-03-14T04:59:04.2583766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2584493Z         x_92: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2585072Z         
2025-03-14T04:59:04.2585411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2587270Z         x_93: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2588903Z         
2025-03-14T04:59:04.2589235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2589979Z         x_94: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_51 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:59:04.2590528Z         
2025-03-14T04:59:04.2590893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2592849Z         x_95: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2594584Z         
2025-03-14T04:59:04.2594952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2595426Z         x_93 += x_95;  out_54: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_93;  x_93 = x_95 = None
2025-03-14T04:59:04.2595674Z         
2025-03-14T04:59:04.2596038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2596524Z         out_55: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:59:04.2596781Z         
2025-03-14T04:59:04.2597112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2597836Z         x_96: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2598385Z         
2025-03-14T04:59:04.2598730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2600577Z         x_97: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2602213Z         
2025-03-14T04:59:04.2602580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2603051Z         out_56: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:59:04.2603304Z         
2025-03-14T04:59:04.2603632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2604379Z         x_98: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_56 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2604920Z         
2025-03-14T04:59:04.2605263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2607147Z         x_99: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2608875Z         
2025-03-14T04:59:04.2609264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2609767Z         out_57: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_99);  x_99 = None
2025-03-14T04:59:04.2610033Z         
2025-03-14T04:59:04.2610386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2611169Z         x_100: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2611845Z         
2025-03-14T04:59:04.2612238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2614318Z         x_101: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2616052Z         
2025-03-14T04:59:04.2616437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2616955Z         x_101 += out_55;  out_58: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_101;  x_101 = out_55 = None
2025-03-14T04:59:04.2617248Z         
2025-03-14T04:59:04.2617631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2618141Z         out_59: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:59:04.2618414Z         
2025-03-14T04:59:04.2618795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2619565Z         x_102: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:59:04.2620140Z         
2025-03-14T04:59:04.2620522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2622502Z         x_103: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2624215Z         
2025-03-14T04:59:04.2624579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2625056Z         out_60: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:59:04.2625312Z         
2025-03-14T04:59:04.2625641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2626389Z         x_104: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_60 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:59:04.2626974Z         
2025-03-14T04:59:04.2627342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2629251Z         x_105: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2630898Z         
2025-03-14T04:59:04.2631265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2631739Z         out_61: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_105);  x_105 = None
2025-03-14T04:59:04.2631996Z         
2025-03-14T04:59:04.2632324Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2633071Z         x_106: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:59:04.2633614Z         
2025-03-14T04:59:04.2633976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:59:04.2635837Z         x_107: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:59:04.2637461Z         
2025-03-14T04:59:04.2637819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:59:04.2638300Z         x_107 += out_59;  out_62: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_107;  x_107 = out_59 = None
2025-03-14T04:59:04.2638566Z         
2025-03-14T04:59:04.2638923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:59:04.2639401Z         out_63: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:59:04.2639657Z         
2025-03-14T04:59:04.2640173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:59:04.2640858Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:59:04.2641129Z         
2025-03-14T04:59:04.2641508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:04.2641987Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:59:04.2642236Z         
2025-03-14T04:59:04.2642761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:59:04.2643394Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:59:04.2643661Z         
2025-03-14T04:59:04.2644035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:04.2644521Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:59:04.2644780Z         
2025-03-14T04:59:04.2645234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:59:04.2645850Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:59:04.2646193Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:59:04.2646474Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:59:04.2646720Z         
2025-03-14T04:59:04.2647173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:59:04.2647731Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:59:04.2647980Z         
2025-03-14T04:59:04.2648427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:59:04.2648983Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:59:04.2649249Z         
2025-03-14T04:59:04.2649733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:59:04.2650462Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:59:04.2650820Z         
2025-03-14T04:59:04.2651439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:59:04.2652128Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:59:04.2652801Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:59:04.2653447Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:59:04.2653758Z         x_108: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:59:04.2654003Z         
2025-03-14T04:59:04.2654404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:59:04.2654939Z         tensor: "f32[82125, 4][4, 1]cpu" = x_108.to(torch.float32);  x_108 = None
2025-03-14T04:59:04.2655202Z         
2025-03-14T04:59:04.2655560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:04.2656711Z         x_109: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_63 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:59:04.2657640Z         
2025-03-14T04:59:04.2658016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:59:04.2658565Z         x_110: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_109, inplace = False);  x_109 = None
2025-03-14T04:59:04.2658887Z         
2025-03-14T04:59:04.2659379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:59:04.2660826Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:59:04.2661777Z         
2025-03-14T04:59:04.2662204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:59:04.2663452Z         x_111: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_110 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:59:04.2664387Z         
2025-03-14T04:59:04.2664811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:59:04.2665356Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:59:04.2665705Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:59:04.2665972Z         
2025-03-14T04:59:04.2666476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:59:04.2667097Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_111.view(4, -1, 4, 73, 75);  x_111 = None
2025-03-14T04:59:04.2667480Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:59:04.2667885Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:59:04.2668181Z         
2025-03-14T04:59:04.2668666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:59:04.2669356Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:59:04.2669684Z         
2025-03-14T04:59:04.2670203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:59:04.2670837Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:59:04.2671186Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:59:04.2671521Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:59:04.2671779Z         
2025-03-14T04:59:04.2672237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:59:04.2672838Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:59:04.2673131Z         
2025-03-14T04:59:04.2673529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:59:04.2674033Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:59:04.2674290Z         
2025-03-14T04:59:04.2674711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:59:04.2675205Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:59:04.2675511Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:04.2675856Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:59:04.2676121Z         
2025-03-14T04:59:04.2676537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:59:04.2677024Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:59:04.2677319Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:59:04.2677638Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:59:04.2677893Z         
2025-03-14T04:59:04.2678283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:59:04.2678761Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:04.2679029Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:59:04.2679289Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:59:04.2679530Z         
2025-03-14T04:59:04.2679920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:59:04.2680436Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:59:04.2680724Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:59:04.2680989Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:59:04.2681233Z         
2025-03-14T04:59:04.2681657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:59:04.2682182Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:59:04.2682511Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:59:04.2682750Z         
2025-03-14T04:59:04.2683139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:59:04.2683639Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:59:04.2683961Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:59:04.2684189Z         
2025-03-14T04:59:04.2684570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:59:04.2685095Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:59:04.2685434Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:59:04.2685676Z         
2025-03-14T04:59:04.2686093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:59:04.2686661Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:59:04.2687030Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:59:04.2687273Z         
2025-03-14T04:59:04.2687716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:59:04.2688334Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:59:04.2688627Z         
2025-03-14T04:59:04.2689128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:59:04.2689781Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:59:04.2690090Z         
2025-03-14T04:59:04.2690575Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:59:04.2691202Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:59:04.2691621Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:59:04.2692007Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:59:04.2692392Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:59:04.2692685Z         
2025-03-14T04:59:04.2693152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:59:04.2693694Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:59:04.2694011Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:59:04.2694383Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:59:04.2694772Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:59:04.2695061Z         
2025-03-14T04:59:04.2695549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:59:04.2696164Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:59:04.2696532Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:59:04.2696918Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:59:04.2697196Z         
2025-03-14T04:59:04.2697716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:59:04.2698319Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:59:04.2698705Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:59:04.2699100Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:59:04.2699385Z         
2025-03-14T04:59:04.2699826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:59:04.2700341Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:59:04.2700648Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:59:04.2700911Z         
2025-03-14T04:59:04.2701348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:59:04.2701830Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:59:04.2702085Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:59:04.2702320Z         
2025-03-14T04:59:04.2702738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:59:04.2703211Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:59:04.2703522Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:59:04.2703766Z         
2025-03-14T04:59:04.2704162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:59:04.2704629Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:59:04.2704915Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:59:04.2705161Z         
2025-03-14T04:59:04.2705590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:59:04.2706166Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:59:04.2706460Z         
2025-03-14T04:59:04.2706870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:59:04.2707412Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:59:04.2707689Z         
2025-03-14T04:59:04.2708151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:59:04.2708745Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:59:04.2709027Z         
2025-03-14T04:59:04.2709587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:59:04.2710273Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:59:04.2710519Z         
2025-03-14T04:59:04.2710892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:04.2711372Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:59:04.2711625Z         
2025-03-14T04:59:04.2712138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:59:04.2712731Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:59:04.2712991Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:59:04.2713255Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:59:04.2713481Z         
2025-03-14T04:59:04.2714018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:59:04.2714705Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:59:04.2715177Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:59:04.2715526Z         
2025-03-14T04:59:04.2716079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:59:04.2716754Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:59:04.2717053Z         
2025-03-14T04:59:04.2717446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:04.2717946Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:59:04.2718206Z         
2025-03-14T04:59:04.2718674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:59:04.2719253Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:59:04.2719514Z         
2025-03-14T04:59:04.2719895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:59:04.2720390Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:59:04.2720653Z         
2025-03-14T04:59:04.2721121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:59:04.2721688Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:59:04.2721938Z         
2025-03-14T04:59:04.2722491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:59:04.2723145Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:59:04.2723446Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:59:04.2723790Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:59:04.2724114Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:59:04.2724358Z         
2025-03-14T04:59:04.2724795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:59:04.2725314Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:59:04.2725538Z         
2025-03-14T04:59:13.5091623Z 
2025-03-14T04:59:13.5097747Z class GraphModule(torch.nn.Module):
2025-03-14T04:59:13.5103506Z     def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:59:13.5105080Z         l_features_res5_ = L_features_res5_
2025-03-14T04:59:13.5105487Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:59:13.5106362Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:59:13.5106901Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:59:13.5107476Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:59:13.5108195Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:59:13.5108770Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:59:13.5109323Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:59:13.5109690Z         
2025-03-14T04:59:13.5110286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:59:13.5110972Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:59:13.5111252Z         
2025-03-14T04:59:13.5111649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:13.5112163Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:59:13.5112417Z         
2025-03-14T04:59:13.5112960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:59:13.5113614Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:59:13.5113885Z         
2025-03-14T04:59:13.5114273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:13.5114812Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:59:13.5115069Z         
2025-03-14T04:59:13.5115537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:59:13.5116152Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:59:13.5116483Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:59:13.5116751Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:59:13.5116990Z         
2025-03-14T04:59:13.5117414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:59:13.5117938Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:59:13.5118182Z         
2025-03-14T04:59:13.5118601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:59:13.5119109Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:59:13.5119345Z         
2025-03-14T04:59:13.5119821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:59:13.5120476Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:59:13.5120810Z         
2025-03-14T04:59:13.5121349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:59:13.5121989Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:59:13.5122537Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:59:13.5123021Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:59:13.5123305Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:59:13.5123536Z         
2025-03-14T04:59:13.5123923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:59:13.5124392Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:59:13.5124629Z         
2025-03-14T04:59:13.5124965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:13.5125885Z         x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:59:13.5126593Z         
2025-03-14T04:59:13.5126947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:59:13.5127455Z         x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:59:13.5127752Z         
2025-03-14T04:59:13.5128211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:59:13.5129319Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:59:13.5130095Z         
2025-03-14T04:59:13.5130549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:59:13.5131714Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:59:13.5132460Z         
2025-03-14T04:59:13.5132886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:59:13.5133423Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:59:13.5133766Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:59:13.5134025Z         
2025-03-14T04:59:13.5134542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:59:13.5135158Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T04:59:13.5135554Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:59:13.5135964Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:59:13.5136261Z         
2025-03-14T04:59:13.5136752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:59:13.5137414Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:59:13.5137737Z         
2025-03-14T04:59:13.5138515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:59:13.5139150Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:59:13.5139497Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:59:13.5139833Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:59:13.5140090Z         
2025-03-14T04:59:13.5140546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:59:13.5141136Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:59:13.5141426Z         
2025-03-14T04:59:13.5141820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:59:13.5142353Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:59:13.5142618Z         
2025-03-14T04:59:13.5143020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:59:13.5143545Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:59:13.5143865Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:13.5144222Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:59:13.5144491Z         
2025-03-14T04:59:13.5144898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:59:13.5145397Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:59:13.5145707Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:59:13.5146032Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:59:13.5146302Z         
2025-03-14T04:59:13.5146702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:59:13.5147328Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:13.5147594Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:59:13.5147851Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:59:13.5148088Z         
2025-03-14T04:59:13.5148500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:59:13.5149010Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:59:13.5149323Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:59:13.5149628Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:59:13.5149871Z         
2025-03-14T04:59:13.5150288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:59:13.5150794Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:59:13.5151119Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:59:13.5151355Z         
2025-03-14T04:59:13.5151739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:59:13.5152238Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:59:13.5152565Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:59:13.5152796Z         
2025-03-14T04:59:13.5153174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:59:13.5153676Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:59:13.5153996Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:59:13.5154228Z         
2025-03-14T04:59:13.5154615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:59:13.5155150Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:59:13.5155517Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:59:13.5155749Z         
2025-03-14T04:59:13.5156163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:59:13.5156695Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:59:13.5156953Z         
2025-03-14T04:59:13.5157361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:59:13.5157880Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:59:13.5158135Z         
2025-03-14T04:59:13.5158560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:59:13.5159102Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:59:13.5159420Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:59:13.5159751Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:59:13.5160099Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:59:13.5160356Z         
2025-03-14T04:59:13.5161122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:59:13.5161746Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:59:13.5162066Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:59:13.5162392Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:59:13.5162771Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:59:13.5163550Z         
2025-03-14T04:59:13.5163979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:59:13.5164499Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:59:13.5164824Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:59:13.5165168Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:59:13.5165422Z         
2025-03-14T04:59:13.5165844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:59:13.5166341Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:59:13.5166671Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:59:13.5167022Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:59:13.5167273Z         
2025-03-14T04:59:13.5167666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:59:13.5168118Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:59:13.5168380Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:59:13.5168615Z         
2025-03-14T04:59:13.5169008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:59:13.5169485Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:59:13.5169741Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:59:13.5169970Z         
2025-03-14T04:59:13.5170361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:59:13.5170842Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:59:13.5171140Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:59:13.5171454Z         
2025-03-14T04:59:13.5171871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:59:13.5172366Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:59:13.5172668Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:59:13.5172921Z         
2025-03-14T04:59:13.5173378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:59:13.5173983Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:59:13.5174291Z         
2025-03-14T04:59:13.5174726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:59:13.5175318Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:59:13.5175621Z         
2025-03-14T04:59:13.5176122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:59:13.5176775Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:59:13.5177092Z         
2025-03-14T04:59:13.5177684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:59:13.5178415Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:59:13.5178678Z         
2025-03-14T04:59:13.5179083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:13.5179603Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:59:13.5179875Z         
2025-03-14T04:59:13.5180415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:59:13.5181045Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:59:13.5181324Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:59:13.5181602Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:59:13.5181830Z         
2025-03-14T04:59:13.5182372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:59:13.5183059Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:59:13.5183525Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:59:13.5183869Z         
2025-03-14T04:59:13.5184409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:59:13.5185082Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:59:13.5185362Z         
2025-03-14T04:59:13.5185739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:13.5186239Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:59:13.5186502Z         
2025-03-14T04:59:13.5186961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:59:13.5187542Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:59:13.5187803Z         
2025-03-14T04:59:13.5188181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:59:13.5188677Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:59:13.5188938Z         
2025-03-14T04:59:13.5189412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:59:13.5189981Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:59:13.5190241Z         
2025-03-14T04:59:13.5190850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:59:13.5191517Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:59:13.5191830Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:59:13.5192153Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:59:13.5192492Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:59:13.5192741Z         
2025-03-14T04:59:13.5193196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:59:13.5193734Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:59:13.5193969Z         
2025-03-14T04:59:13.5194111Z 
2025-03-14T04:59:13.5194199Z class GraphModule(torch.nn.Module):
2025-03-14T04:59:13.5195534Z     def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:59:13.5196863Z         l_features_res5_ = L_features_res5_
2025-03-14T04:59:13.5197264Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:59:13.5197813Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:59:13.5198309Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:59:13.5198850Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:59:13.5199454Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:59:13.5200080Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:59:13.5200653Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:59:13.5201030Z         
2025-03-14T04:59:13.5201590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:59:13.5202256Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:59:13.5202535Z         
2025-03-14T04:59:13.5202931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:13.5203449Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:59:13.5203710Z         
2025-03-14T04:59:13.5204250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:59:13.5204964Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:59:13.5205233Z         
2025-03-14T04:59:13.5205625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:13.5206127Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:59:13.5206393Z         
2025-03-14T04:59:13.5206872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:59:13.5207506Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:59:13.5207851Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:59:13.5208131Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:59:13.5208374Z         
2025-03-14T04:59:13.5208811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:59:13.5209348Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:59:13.5209596Z         
2025-03-14T04:59:13.5210027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:59:13.5210551Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:59:13.5210798Z         
2025-03-14T04:59:13.5211293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:59:13.5212068Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:59:13.5212412Z         
2025-03-14T04:59:13.5212938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:59:13.5213560Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:59:13.5214084Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:59:13.5214596Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:59:13.5214900Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:59:13.5215145Z         
2025-03-14T04:59:13.5215545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:59:13.5216036Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:59:13.5216304Z         
2025-03-14T04:59:13.5216657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:59:13.5217637Z         x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:59:13.5218403Z         
2025-03-14T04:59:13.5218759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:59:13.5219304Z         x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:59:13.5219607Z         
2025-03-14T04:59:13.5220078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:59:13.5221149Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:59:13.5221907Z         
2025-03-14T04:59:13.5222359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:59:13.5223369Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:59:13.5224080Z         
2025-03-14T04:59:13.5224511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:59:13.5225062Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:59:13.5225421Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:59:13.5225676Z         
2025-03-14T04:59:13.5226168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:59:13.5226777Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T04:59:13.5227155Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:59:13.5227550Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:59:13.5227840Z         
2025-03-14T04:59:13.5228317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:59:13.5228976Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:59:13.5229293Z         
2025-03-14T04:59:13.5229804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:59:13.5230430Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:59:13.5230770Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:59:13.5231125Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:59:13.5231383Z         
2025-03-14T04:59:13.5231844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:59:13.5232456Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:59:13.5232741Z         
2025-03-14T04:59:13.5233133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:59:13.5233637Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:59:13.5233894Z         
2025-03-14T04:59:13.5234291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:59:13.5234789Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:59:13.5235095Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:13.5235419Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:59:13.5235682Z         
2025-03-14T04:59:13.5236081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:59:13.5236580Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:59:13.5236878Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:59:13.5237198Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:59:13.5237460Z         
2025-03-14T04:59:13.5237851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:59:13.5238348Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:13.5238610Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:59:13.5238867Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:59:13.5239104Z         
2025-03-14T04:59:13.5239494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:59:13.5239992Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:59:13.5240280Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:59:13.5240545Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:59:13.5240786Z         
2025-03-14T04:59:13.5241195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:59:13.5241700Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:59:13.5242022Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:59:13.5242247Z         
2025-03-14T04:59:13.5242630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:59:13.5243127Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:59:13.5243448Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:59:13.5243678Z         
2025-03-14T04:59:13.5244077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:59:13.5244577Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:59:13.5244918Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:59:13.5255746Z         
2025-03-14T04:59:13.5256352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:59:13.5256918Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:59:13.5257280Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:59:13.5257524Z         
2025-03-14T04:59:13.5257961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:59:13.5258493Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:59:13.5258753Z         
2025-03-14T04:59:13.5259169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:59:13.5259691Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:59:13.5259943Z         
2025-03-14T04:59:13.5260370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:59:13.5261139Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:59:13.5261466Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:59:13.5261807Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:59:13.5262164Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:59:13.5262641Z         
2025-03-14T04:59:13.5263082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:59:13.5263622Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:59:13.5263948Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:59:13.5264278Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:59:13.5264628Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:59:13.5264886Z         
2025-03-14T04:59:13.5265299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:59:13.5265800Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:59:13.5266129Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:59:13.5266477Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:59:13.5266727Z         
2025-03-14T04:59:13.5267142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:59:13.5267641Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:59:13.5267976Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:59:13.5268374Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:59:13.5268625Z         
2025-03-14T04:59:13.5269038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:59:13.5269538Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:59:13.5269825Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:59:13.5270061Z         
2025-03-14T04:59:13.5270450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:59:13.5270909Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:59:13.5271170Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:59:13.5271403Z         
2025-03-14T04:59:13.5271789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:59:13.5272257Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:59:13.5272550Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:59:13.5272794Z         
2025-03-14T04:59:13.5273180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:59:13.5273646Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:59:13.5273934Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:59:13.5274176Z         
2025-03-14T04:59:13.5274603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:59:13.5275181Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:59:13.5275490Z         
2025-03-14T04:59:13.5275908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:59:13.5276456Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:59:13.5276735Z         
2025-03-14T04:59:13.5277203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:59:13.5277807Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:59:13.5278094Z         
2025-03-14T04:59:13.5278660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:59:13.5279336Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:59:13.5279582Z         
2025-03-14T04:59:13.5279958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:13.5280441Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:59:13.5280699Z         
2025-03-14T04:59:13.5281220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:59:13.5281834Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:59:13.5282105Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:59:13.5282372Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:59:13.5282619Z         
2025-03-14T04:59:13.5283188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:59:13.5283866Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:59:13.5284314Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:59:13.5284660Z         
2025-03-14T04:59:13.5285211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:59:13.5285884Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:59:13.5286168Z         
2025-03-14T04:59:13.5286550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:13.5287046Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:59:13.5287308Z         
2025-03-14T04:59:13.5287775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:59:13.5288352Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:59:13.5288612Z         
2025-03-14T04:59:13.5288990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:59:13.5289497Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:59:13.5289757Z         
2025-03-14T04:59:13.5290219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:59:13.5290792Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:59:13.5291052Z         
2025-03-14T04:59:13.5291704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:59:13.5292419Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:59:13.5292740Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:59:13.5293092Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:59:13.5293429Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:59:13.5293688Z         
2025-03-14T04:59:13.5294150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:59:13.5294693Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:59:13.5294928Z         
2025-03-14T04:59:14.9391323Z 
2025-03-14T04:59:14.9396356Z class GraphModule(torch.nn.Module):
2025-03-14T04:59:14.9402064Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"):
2025-03-14T04:59:14.9403089Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T04:59:14.9403463Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T04:59:14.9403750Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T04:59:14.9404002Z         
2025-03-14T04:59:14.9404687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:59:14.9405432Z         pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T04:59:14.9405778Z         
2025-03-14T04:59:14.9406365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:59:14.9407093Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T04:59:14.9407507Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:59:14.9407860Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:59:14.9408125Z         
2025-03-14T04:59:14.9408650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:59:14.9409310Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T04:59:14.9409661Z         
2025-03-14T04:59:14.9410054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:59:14.9410555Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:59:14.9410866Z         
2025-03-14T04:59:14.9411277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:59:14.9411912Z         getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:59:14.9412232Z         getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:14.9412575Z         widths: "f32[328500][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T04:59:14.9412834Z         
2025-03-14T04:59:14.9413257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:59:14.9413749Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:59:14.9414049Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:59:14.9414370Z         heights: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:59:14.9414637Z         
2025-03-14T04:59:14.9415030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:59:14.9415511Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:14.9415772Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:59:14.9416028Z         ctr_x: "f32[328500][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T04:59:14.9416270Z         
2025-03-14T04:59:14.9416743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:59:14.9417253Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:59:14.9417541Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:59:14.9417854Z         ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T04:59:14.9418102Z         
2025-03-14T04:59:14.9418549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:59:14.9419056Z         getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:59:14.9419381Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T04:59:14.9419613Z         
2025-03-14T04:59:14.9419995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:59:14.9420490Z         getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:59:14.9420805Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T04:59:14.9421035Z         
2025-03-14T04:59:14.9421412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:59:14.9421908Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:59:14.9422217Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:59:14.9422445Z         
2025-03-14T04:59:14.9422828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:59:14.9423352Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:59:14.9423696Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:59:14.9423924Z         
2025-03-14T04:59:14.9424371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:59:14.9424902Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:59:14.9425158Z         
2025-03-14T04:59:14.9425579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:59:14.9426095Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:59:14.9426348Z         
2025-03-14T04:59:14.9426783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:59:14.9427323Z         getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:59:14.9427647Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T04:59:14.9427974Z         getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:59:14.9428321Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T04:59:14.9428581Z         
2025-03-14T04:59:14.9429010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:59:14.9429553Z         getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:59:14.9429891Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T04:59:14.9430226Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:59:14.9430570Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T04:59:14.9430848Z         
2025-03-14T04:59:14.9431279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:59:14.9431786Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:59:14.9432116Z         getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:59:14.9432463Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T04:59:14.9432714Z         
2025-03-14T04:59:14.9433130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:59:14.9433631Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:59:14.9433968Z         getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:59:14.9434325Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T04:59:14.9434576Z         
2025-03-14T04:59:14.9434974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:59:14.9435435Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:59:14.9435695Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:59:14.9435928Z         
2025-03-14T04:59:14.9436321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:59:14.9436779Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:59:14.9437078Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:59:14.9437312Z         
2025-03-14T04:59:14.9437699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:59:14.9438178Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:59:14.9438460Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:59:14.9438705Z         
2025-03-14T04:59:14.9439084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:59:14.9439548Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:59:14.9439826Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:59:14.9440070Z         
2025-03-14T04:59:14.9440495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:59:14.9441085Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:59:14.9441370Z         
2025-03-14T04:59:14.9441779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:59:14.9442332Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:59:14.9442607Z         
2025-03-14T04:59:14.9443105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:59:14.9443725Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:59:14.9444028Z         
2025-03-14T04:59:14.9444612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:59:14.9445317Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:59:14.9445551Z         
2025-03-14T04:59:14.9445927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:14.9446405Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:59:14.9446658Z         
2025-03-14T04:59:14.9447174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:59:14.9447828Z         topk = l_pred_objectness_logits_0_.topk(6000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T04:59:14.9448156Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:59:14.9448422Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:59:14.9448647Z         
2025-03-14T04:59:14.9449186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:59:14.9449857Z         getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:59:14.9450299Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)];  proposals_i_1 = getitem_18 = topk_idx = None
2025-03-14T04:59:14.9450667Z         
2025-03-14T04:59:14.9451219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:59:14.9452035Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:59:14.9452361Z         
2025-03-14T04:59:14.9452751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:59:14.9453265Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:59:14.9453532Z         
2025-03-14T04:59:14.9454002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:59:14.9454586Z         getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:59:14.9454851Z         
2025-03-14T04:59:14.9455233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:59:14.9455707Z         tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32);  getitem_20 = None
2025-03-14T04:59:14.9455961Z         
2025-03-14T04:59:14.9456420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:59:14.9457006Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:59:14.9457261Z         
2025-03-14T04:59:14.9457827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:59:14.9458530Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T04:59:14.9458838Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:59:14.9459158Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:59:14.9459492Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:59:14.9459745Z         
2025-03-14T04:59:14.9460198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:59:14.9461077Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:59:14.9461320Z         
2025-03-14T04:59:21.9346075Z 
2025-03-14T04:59:21.9347659Z class GraphModule(torch.nn.Module):
2025-03-14T04:59:21.9350204Z     def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"):
2025-03-14T04:59:21.9352850Z         l_stack0_ = L_stack0_
2025-03-14T04:59:21.9353235Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:59:21.9353782Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:59:21.9354283Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:59:21.9354734Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:59:21.9355235Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:59:21.9355791Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:59:21.9356346Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:59:21.9356896Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:59:21.9357371Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9357768Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9358160Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9358600Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9358895Z         
2025-03-14T04:59:21.9359297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:59:21.9359885Z         x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:59:21.9360826Z         x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:59:21.9361557Z         x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:59:21.9362279Z         x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:59:21.9363078Z         x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:59:21.9363355Z         
2025-03-14T04:59:21.9363762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:59:21.9364719Z         scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:59:21.9365424Z         
2025-03-14T04:59:21.9365848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:59:21.9366952Z         proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:59:21.9367736Z         
2025-03-14T04:59:21.9368156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:59:21.9368671Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:59:21.9368956Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:59:21.9369206Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:59:21.9369513Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:59:21.9369806Z         getitem_2: "Sym(1230 - s0)" = size_1[0]
2025-03-14T04:59:21.9370083Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:59:21.9370362Z         
2025-03-14T04:59:21.9370776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:59:21.9371904Z         proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:59:21.9372703Z         
2025-03-14T04:59:21.9373248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:59:21.9373893Z         deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:59:21.9374223Z         
2025-03-14T04:59:21.9374660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:59:21.9375269Z         boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:59:21.9375576Z         
2025-03-14T04:59:21.9376026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:59:21.9376582Z         getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:59:21.9376926Z         getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:21.9377307Z         widths: "f32[3230][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:59:21.9377588Z         
2025-03-14T04:59:21.9378039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:59:21.9378594Z         getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:59:21.9378916Z         getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:59:21.9379272Z         heights: "f32[3230][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:59:21.9379568Z         
2025-03-14T04:59:21.9380010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:59:21.9380545Z         getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:21.9380824Z         mul: "f32[3230][1]cpu" = 0.5 * widths
2025-03-14T04:59:21.9381076Z         ctr_x: "f32[3230][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:59:21.9381333Z         
2025-03-14T04:59:21.9381730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:59:21.9382235Z         getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:59:21.9382516Z         mul_1: "f32[3230][1]cpu" = 0.5 * heights
2025-03-14T04:59:21.9382779Z         ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:59:21.9383021Z         
2025-03-14T04:59:21.9383441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:59:21.9383956Z         getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:59:21.9384284Z         dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:59:21.9384515Z         
2025-03-14T04:59:21.9384902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:59:21.9385411Z         getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:59:21.9385734Z         dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:59:21.9385962Z         
2025-03-14T04:59:21.9386344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:59:21.9386840Z         getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:59:21.9387275Z         dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:59:21.9387594Z         
2025-03-14T04:59:21.9388186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:59:21.9388758Z         getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:59:21.9389127Z         dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:59:21.9389375Z         
2025-03-14T04:59:21.9390037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:59:21.9390873Z         dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:59:21.9391127Z         
2025-03-14T04:59:21.9391557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:59:21.9392102Z         dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:59:21.9392356Z         
2025-03-14T04:59:21.9392789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:59:21.9393330Z         getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:59:21.9393641Z         mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:59:21.9393967Z         getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:59:21.9394308Z         pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:59:21.9394558Z         
2025-03-14T04:59:21.9394992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:59:21.9395550Z         getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:59:21.9395858Z         mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:59:21.9396175Z         getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:59:21.9396535Z         pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:59:21.9396784Z         
2025-03-14T04:59:21.9397219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:59:21.9397728Z         exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:59:21.9398055Z         getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:59:21.9398399Z         pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:59:21.9398659Z         
2025-03-14T04:59:21.9399090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:59:21.9399607Z         exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:59:21.9399939Z         getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:59:21.9400291Z         pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:59:21.9400547Z         
2025-03-14T04:59:21.9400979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:59:21.9401459Z         mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:59:21.9401731Z         x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:59:21.9402023Z         
2025-03-14T04:59:21.9402512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:59:21.9402976Z         mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:59:21.9403241Z         y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:59:21.9403482Z         
2025-03-14T04:59:21.9403872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:59:21.9404364Z         mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:59:21.9404669Z         x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:59:21.9404924Z         
2025-03-14T04:59:21.9405332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:59:21.9405831Z         mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:59:21.9406128Z         y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:59:21.9406383Z         
2025-03-14T04:59:21.9406833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:59:21.9407545Z         pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:59:21.9407854Z         
2025-03-14T04:59:21.9408295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:59:21.9408883Z         predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320));  pred_boxes = None
2025-03-14T04:59:21.9409211Z         
2025-03-14T04:59:21.9409688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:59:21.9410329Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:59:21.9410712Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:59:21.9411011Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:59:21.9411347Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:59:21.9411824Z         getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:59:21.9412099Z         
2025-03-14T04:59:21.9412505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:59:21.9413084Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:59:21.9413432Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:59:21.9413675Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:59:21.9414036Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:59:21.9414389Z         getitem_26: "Sym(1230 - s0)" = size_3[0]
2025-03-14T04:59:21.9414639Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:59:21.9414858Z         
2025-03-14T04:59:21.9415306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:59:21.9415949Z         probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:59:21.9416270Z         
2025-03-14T04:59:21.9416736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:59:21.9417354Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:59:21.9417720Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:59:21.9418018Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:59:21.9418320Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:59:21.9418641Z         getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:59:21.9418903Z         
2025-03-14T04:59:21.9419470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:59:21.9420187Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:59:21.9420531Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:59:21.9420875Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:59:21.9421217Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:59:21.9421515Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:59:21.9421748Z         
2025-03-14T04:59:21.9422197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:59:21.9422742Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:59:21.9422978Z         
2025-03-14T04:59:21.9423071Z 
2025-03-14T04:59:21.9423159Z class GraphModule(torch.nn.Module):
2025-03-14T04:59:21.9425132Z     def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"):
2025-03-14T04:59:21.9427224Z         l_stack0_ = L_stack0_
2025-03-14T04:59:21.9427574Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:59:21.9428062Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:59:21.9428545Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:59:21.9429028Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:59:21.9429534Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:59:21.9430148Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:59:21.9430699Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:59:21.9431240Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:59:21.9431708Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9432099Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9432490Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9432878Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9433164Z         
2025-03-14T04:59:21.9433524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:59:21.9433989Z         x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:59:21.9434678Z         x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:59:21.9435391Z         x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:59:21.9436102Z         x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:59:21.9436834Z         x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:59:21.9437112Z         
2025-03-14T04:59:21.9437513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:59:21.9438522Z         scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:59:21.9439231Z         
2025-03-14T04:59:21.9440726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:59:21.9442721Z         proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:59:21.9443521Z         
2025-03-14T04:59:21.9443928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:59:21.9444420Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:59:21.9444741Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:59:21.9444983Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:59:21.9445268Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:59:21.9445571Z         getitem_2: "Sym(1230 - s0)" = size_1[0]
2025-03-14T04:59:21.9445830Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:59:21.9446126Z         
2025-03-14T04:59:21.9446541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:59:21.9447552Z         proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:59:21.9448358Z         
2025-03-14T04:59:21.9448879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:59:21.9449518Z         deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:59:21.9449803Z         
2025-03-14T04:59:21.9450224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:59:21.9450778Z         boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:59:21.9451080Z         
2025-03-14T04:59:21.9451581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:59:21.9452144Z         getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:59:21.9452493Z         getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:21.9452847Z         widths: "f32[3230][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:59:21.9453142Z         
2025-03-14T04:59:21.9453570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:59:21.9454088Z         getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:59:21.9454398Z         getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:59:21.9454728Z         heights: "f32[3230][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:59:21.9454999Z         
2025-03-14T04:59:21.9455414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:59:21.9455916Z         getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:21.9456182Z         mul: "f32[3230][1]cpu" = 0.5 * widths
2025-03-14T04:59:21.9456449Z         ctr_x: "f32[3230][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:59:21.9456689Z         
2025-03-14T04:59:21.9457104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:59:21.9457629Z         getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:59:21.9457923Z         mul_1: "f32[3230][1]cpu" = 0.5 * heights
2025-03-14T04:59:21.9458194Z         ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:59:21.9458447Z         
2025-03-14T04:59:21.9458915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:59:21.9459458Z         getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:59:21.9459799Z         dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:59:21.9460065Z         
2025-03-14T04:59:21.9460508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:59:21.9461223Z         getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:59:21.9461562Z         dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:59:21.9461810Z         
2025-03-14T04:59:21.9462215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:59:21.9462725Z         getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:59:21.9463059Z         dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:59:21.9463317Z         
2025-03-14T04:59:21.9463703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:59:21.9464234Z         getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:59:21.9464576Z         dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:59:21.9464804Z         
2025-03-14T04:59:21.9465223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:59:21.9465747Z         dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:59:21.9466005Z         
2025-03-14T04:59:21.9466420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:59:21.9467007Z         dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:59:21.9467253Z         
2025-03-14T04:59:21.9467698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:59:21.9468262Z         getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:59:21.9468586Z         mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:59:21.9468924Z         getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:59:21.9469264Z         pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:59:21.9469516Z         
2025-03-14T04:59:21.9469946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:59:21.9470478Z         getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:59:21.9470785Z         mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:59:21.9471107Z         getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:59:21.9471442Z         pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:59:21.9471688Z         
2025-03-14T04:59:21.9472124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:59:21.9472627Z         exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:59:21.9472950Z         getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:59:21.9473309Z         pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:59:21.9473555Z         
2025-03-14T04:59:21.9473986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:59:21.9474487Z         exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:59:21.9474814Z         getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:59:21.9475164Z         pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:59:21.9475418Z         
2025-03-14T04:59:21.9475809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:59:21.9476284Z         mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:59:21.9476538Z         x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:59:21.9476772Z         
2025-03-14T04:59:21.9477165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:59:21.9477627Z         mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:59:21.9477884Z         y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:59:21.9478114Z         
2025-03-14T04:59:21.9478502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:59:21.9478974Z         mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:59:21.9479259Z         x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:59:21.9479523Z         
2025-03-14T04:59:21.9479906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:59:21.9480380Z         mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:59:21.9480666Z         y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:59:21.9480906Z         
2025-03-14T04:59:21.9481343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:59:21.9481922Z         pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:59:21.9482210Z         
2025-03-14T04:59:21.9482625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:59:21.9483177Z         predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320));  pred_boxes = None
2025-03-14T04:59:21.9483462Z         
2025-03-14T04:59:21.9483904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:59:21.9484511Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:59:21.9484869Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:59:21.9485157Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:59:21.9485474Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:59:21.9485789Z         getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:59:21.9486067Z         
2025-03-14T04:59:21.9486433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:59:21.9486996Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:59:21.9487361Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:59:21.9487609Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:59:21.9487986Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:59:21.9488405Z         getitem_26: "Sym(1230 - s0)" = size_3[0]
2025-03-14T04:59:21.9488761Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:59:21.9489091Z         
2025-03-14T04:59:21.9489545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:59:21.9490172Z         probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:59:21.9490470Z         
2025-03-14T04:59:21.9490925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:59:21.9491623Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:59:21.9492012Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:59:21.9492329Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:59:21.9492664Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:59:21.9492997Z         getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:59:21.9493308Z         
2025-03-14T04:59:21.9493871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:59:21.9494578Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:59:21.9494921Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:59:21.9495266Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:59:21.9495610Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:59:21.9495908Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:59:21.9496148Z         
2025-03-14T04:59:21.9496587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:59:21.9497120Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:59:21.9497357Z         
2025-03-14T04:59:21.9497506Z 
2025-03-14T04:59:21.9497602Z class GraphModule(torch.nn.Module):
2025-03-14T04:59:21.9499615Z     def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"):
2025-03-14T04:59:21.9501740Z         l_stack0_ = L_stack0_
2025-03-14T04:59:21.9502094Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:59:21.9502587Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:59:21.9503073Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:59:21.9503557Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:59:21.9504088Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:59:21.9504639Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:59:21.9505187Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:59:21.9505735Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:59:21.9506205Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9506664Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9507328Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9507833Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9508119Z         
2025-03-14T04:59:21.9508484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:59:21.9508948Z         x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:59:21.9509641Z         x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:59:21.9510345Z         x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:59:21.9511052Z         x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:59:21.9511928Z         x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:59:21.9512201Z         
2025-03-14T04:59:21.9512616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:59:21.9513641Z         scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:59:21.9514400Z         
2025-03-14T04:59:21.9514831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:59:21.9515827Z         proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:59:21.9516615Z         
2025-03-14T04:59:21.9517008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:59:21.9517502Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:59:21.9517759Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:59:21.9518000Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:59:21.9518286Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:59:21.9518562Z         getitem_2: "Sym(1230 - s0)" = size_1[0]
2025-03-14T04:59:21.9518803Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:59:21.9519028Z         
2025-03-14T04:59:21.9519393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:59:21.9520327Z         proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:59:21.9521038Z         
2025-03-14T04:59:21.9521512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:59:21.9522090Z         deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:59:21.9522360Z         
2025-03-14T04:59:21.9522754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:59:21.9523275Z         boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:59:21.9523552Z         
2025-03-14T04:59:21.9523949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:59:21.9524445Z         getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:59:21.9524753Z         getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:21.9525069Z         widths: "f32[3230][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:59:21.9525327Z         
2025-03-14T04:59:21.9525734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:59:21.9526227Z         getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:59:21.9526521Z         getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:59:21.9526832Z         heights: "f32[3230][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:59:21.9527103Z         
2025-03-14T04:59:21.9527535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:59:21.9528043Z         getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:21.9528326Z         mul: "f32[3230][1]cpu" = 0.5 * widths
2025-03-14T04:59:21.9528587Z         ctr_x: "f32[3230][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:59:21.9528849Z         
2025-03-14T04:59:21.9529265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:59:21.9529803Z         getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:59:21.9530098Z         mul_1: "f32[3230][1]cpu" = 0.5 * heights
2025-03-14T04:59:21.9530369Z         ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:59:21.9530621Z         
2025-03-14T04:59:21.9531043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:59:21.9531695Z         getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:59:21.9532059Z         dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:59:21.9532316Z         
2025-03-14T04:59:21.9532744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:59:21.9533277Z         getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:59:21.9533613Z         dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:59:21.9533855Z         
2025-03-14T04:59:21.9534252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:59:21.9534797Z         getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:59:21.9535145Z         dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:59:21.9535391Z         
2025-03-14T04:59:21.9535809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:59:21.9536391Z         getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:59:21.9536758Z         dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:59:21.9537006Z         
2025-03-14T04:59:21.9537465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:59:21.9538033Z         dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:59:21.9538307Z         
2025-03-14T04:59:21.9538755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:59:21.9539320Z         dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:59:21.9539591Z         
2025-03-14T04:59:21.9540053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:59:21.9540626Z         getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:59:21.9540958Z         mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:59:21.9541311Z         getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:59:21.9541650Z         pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:59:21.9541922Z         
2025-03-14T04:59:21.9542371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:59:21.9542899Z         getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:59:21.9543204Z         mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:59:21.9543523Z         getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:59:21.9543855Z         pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:59:21.9544100Z         
2025-03-14T04:59:21.9544513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:59:21.9545005Z         exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:59:21.9545319Z         getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:59:21.9545652Z         pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:59:21.9545893Z         
2025-03-14T04:59:21.9546300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:59:21.9546790Z         exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:59:21.9547113Z         getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:59:21.9547459Z         pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:59:21.9547708Z         
2025-03-14T04:59:21.9548097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:59:21.9548600Z         mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:59:21.9548857Z         x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:59:21.9549089Z         
2025-03-14T04:59:21.9549482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:59:21.9549934Z         mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:59:21.9550186Z         y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:59:21.9550417Z         
2025-03-14T04:59:21.9550810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:59:21.9551275Z         mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:59:21.9551566Z         x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:59:21.9551799Z         
2025-03-14T04:59:21.9552183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:59:21.9552645Z         mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:59:21.9552927Z         y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:59:21.9553172Z         
2025-03-14T04:59:21.9553620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:59:21.9554197Z         pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:59:21.9554484Z         
2025-03-14T04:59:21.9554920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:59:21.9555502Z         predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320));  pred_boxes = None
2025-03-14T04:59:21.9555785Z         
2025-03-14T04:59:21.9556227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:59:21.9556840Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:59:21.9557200Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:59:21.9557482Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:59:21.9557782Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:59:21.9558098Z         getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:59:21.9558354Z         
2025-03-14T04:59:21.9558732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:59:21.9559283Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:59:21.9559627Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:59:21.9559859Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:59:21.9560221Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:59:21.9560759Z         getitem_26: "Sym(1230 - s0)" = size_3[0]
2025-03-14T04:59:21.9561014Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:59:21.9561285Z         
2025-03-14T04:59:21.9561701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:59:21.9562257Z         probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:59:21.9562541Z         
2025-03-14T04:59:21.9562977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:59:21.9563638Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:59:21.9563991Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:59:21.9564271Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:59:21.9564557Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:59:21.9564867Z         getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:59:21.9565124Z         
2025-03-14T04:59:21.9565674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:59:21.9566359Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:59:21.9566694Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:59:21.9567030Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:59:21.9567400Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:59:21.9567701Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:59:21.9567979Z         
2025-03-14T04:59:21.9568448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:59:21.9568979Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:59:21.9569204Z         
2025-03-14T04:59:21.9569341Z 
2025-03-14T04:59:21.9569436Z class GraphModule(torch.nn.Module):
2025-03-14T04:59:21.9571503Z     def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"):
2025-03-14T04:59:21.9573716Z         l_stack0_ = L_stack0_
2025-03-14T04:59:21.9574049Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:59:21.9574531Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:59:21.9575004Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:59:21.9575495Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:59:21.9576017Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:59:21.9576596Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:59:21.9577166Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:59:21.9577727Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:59:21.9578212Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9578616Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9579013Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9579396Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:59:21.9579685Z         
2025-03-14T04:59:21.9580054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:59:21.9580531Z         x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:59:21.9581267Z         x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:59:21.9582004Z         x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:59:21.9582765Z         x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:59:21.9583484Z         x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:59:21.9583762Z         
2025-03-14T04:59:21.9584163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:59:21.9585137Z         scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:59:21.9585856Z         
2025-03-14T04:59:21.9586272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:59:21.9587276Z         proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:59:21.9588016Z         
2025-03-14T04:59:21.9588389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:59:21.9588856Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:59:21.9589105Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:59:21.9589335Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:59:21.9589608Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:59:21.9589867Z         getitem_2: "Sym(1230 - s0)" = size_1[0]
2025-03-14T04:59:21.9590107Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:59:21.9590315Z         
2025-03-14T04:59:21.9590678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:59:21.9591612Z         proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:59:21.9592321Z         
2025-03-14T04:59:21.9592774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:59:21.9593340Z         deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:59:21.9593608Z         
2025-03-14T04:59:21.9593995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:59:21.9594507Z         boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:59:21.9594771Z         
2025-03-14T04:59:21.9595178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:59:21.9595672Z         getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:59:21.9595991Z         getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:21.9596316Z         widths: "f32[3230][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:59:21.9596576Z         
2025-03-14T04:59:21.9596984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:59:21.9597472Z         getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:59:21.9597764Z         getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:59:21.9598078Z         heights: "f32[3230][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:59:21.9598336Z         
2025-03-14T04:59:21.9598727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:59:21.9599220Z         getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:21.9599475Z         mul: "f32[3230][1]cpu" = 0.5 * widths
2025-03-14T04:59:21.9599723Z         ctr_x: "f32[3230][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:59:21.9599954Z         
2025-03-14T04:59:21.9600345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:59:21.9600855Z         getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:59:21.9601133Z         mul_1: "f32[3230][1]cpu" = 0.5 * heights
2025-03-14T04:59:21.9601395Z         ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:59:21.9601631Z         
2025-03-14T04:59:21.9602026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:59:21.9602567Z         getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:59:21.9602889Z         dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:59:21.9603121Z         
2025-03-14T04:59:21.9603506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:59:21.9604011Z         getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:59:21.9604330Z         dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:59:21.9604562Z         
2025-03-14T04:59:21.9604944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:59:21.9605446Z         getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:59:21.9605767Z         dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:59:21.9605994Z         
2025-03-14T04:59:21.9606394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:59:21.9606953Z         getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:59:21.9607311Z         dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:59:21.9607548Z         
2025-03-14T04:59:21.9608005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:59:21.9608556Z         dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:59:21.9608847Z         
2025-03-14T04:59:21.9609295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:59:21.9609837Z         dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:59:21.9610093Z         
2025-03-14T04:59:21.9610537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:59:21.9611095Z         getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:59:21.9611535Z         mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:59:21.9611920Z         getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:59:21.9612307Z         pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:59:21.9612598Z         
2025-03-14T04:59:21.9613088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:59:21.9613695Z         getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:59:21.9614025Z         mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:59:21.9614388Z         getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:59:21.9614762Z         pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:59:21.9615037Z         
2025-03-14T04:59:21.9615498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:59:21.9616075Z         exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:59:21.9616433Z         getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:59:21.9616812Z         pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:59:21.9617083Z         
2025-03-14T04:59:21.9617539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:59:21.9618089Z         exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:59:21.9618454Z         getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:59:21.9618834Z         pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:59:21.9619111Z         
2025-03-14T04:59:21.9619550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:59:21.9620064Z         mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:59:21.9620351Z         x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:59:21.9620605Z         
2025-03-14T04:59:21.9621037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:59:21.9621542Z         mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:59:21.9621846Z         y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:59:21.9622111Z         
2025-03-14T04:59:21.9622542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:59:21.9623085Z         mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:59:21.9623467Z         x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:59:21.9623739Z         
2025-03-14T04:59:21.9624173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:59:21.9624691Z         mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:59:21.9625003Z         y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:59:21.9625244Z         
2025-03-14T04:59:21.9625668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:59:21.9626239Z         pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:59:21.9626526Z         
2025-03-14T04:59:21.9626942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:59:21.9627500Z         predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320));  pred_boxes = None
2025-03-14T04:59:21.9627794Z         
2025-03-14T04:59:21.9628250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:59:21.9628852Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:59:21.9629217Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:59:21.9629527Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:59:21.9629855Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:59:21.9630210Z         getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:59:21.9630491Z         
2025-03-14T04:59:21.9630909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:59:21.9631502Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:59:21.9631858Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:59:21.9632111Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:59:21.9632491Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:59:21.9632855Z         getitem_26: "Sym(1230 - s0)" = size_3[0]
2025-03-14T04:59:21.9633112Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:59:21.9633339Z         
2025-03-14T04:59:21.9633772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:59:21.9634343Z         probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:59:21.9634637Z         
2025-03-14T04:59:21.9635090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:59:21.9635727Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:59:21.9636105Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:59:21.9636412Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:59:21.9636757Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:59:21.9637119Z         getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:59:21.9637407Z         
2025-03-14T04:59:21.9638017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:59:21.9638781Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:59:21.9639162Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:59:21.9639548Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:59:21.9639908Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:59:21.9640222Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:59:21.9640469Z         
2025-03-14T04:59:21.9640923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:59:21.9641463Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:59:21.9641706Z         
2025-03-14T04:59:24.0180912Z 
2025-03-14T04:59:24.0184152Z class GraphModule(torch.nn.Module):
2025-03-14T04:59:24.0189479Z     def forward(self, L_predictions_0_: "f32[3230, 81][81, 1]cpu", L_predictions_1_: "f32[3230, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"):
2025-03-14T04:59:24.0191360Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:59:24.0191668Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:59:24.0192027Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:59:24.0192439Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:59:24.0192851Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:59:24.0193241Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:59:24.0193528Z         
2025-03-14T04:59:24.0193949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:59:24.0194426Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:59:24.0194685Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:59:24.0194916Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:59:24.0195203Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:59:24.0195460Z         getitem_2: "Sym(1230 - s0)" = size_1[0]
2025-03-14T04:59:24.0195697Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:59:24.0195915Z         
2025-03-14T04:59:24.0196321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:59:24.0197459Z         proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:59:24.0198389Z         
2025-03-14T04:59:24.0198928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:59:24.0199567Z         deltas: "f32[3230, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:59:24.0199828Z         
2025-03-14T04:59:24.0200227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:59:24.0200753Z         boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:59:24.0201022Z         
2025-03-14T04:59:24.0201420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:59:24.0201919Z         getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:59:24.0202218Z         getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:24.0202530Z         widths: "f32[3230][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:59:24.0202788Z         
2025-03-14T04:59:24.0203176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:59:24.0203655Z         getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:59:24.0203940Z         getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:59:24.0204249Z         heights: "f32[3230][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:59:24.0204505Z         
2025-03-14T04:59:24.0204887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:59:24.0205383Z         getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:24.0205634Z         mul: "f32[3230][1]cpu" = 0.5 * widths
2025-03-14T04:59:24.0205890Z         ctr_x: "f32[3230][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:59:24.0206124Z         
2025-03-14T04:59:24.0206517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:59:24.0207016Z         getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:59:24.0207299Z         mul_1: "f32[3230][1]cpu" = 0.5 * heights
2025-03-14T04:59:24.0207559Z         ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:59:24.0207801Z         
2025-03-14T04:59:24.0208218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:59:24.0208732Z         getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:59:24.0209057Z         dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:59:24.0209291Z         
2025-03-14T04:59:24.0209675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:59:24.0210180Z         getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:59:24.0210502Z         dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:59:24.0210739Z         
2025-03-14T04:59:24.0211147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:59:24.0211837Z         getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:59:24.0212214Z         dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:59:24.0212468Z         
2025-03-14T04:59:24.0212893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:59:24.0213435Z         getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:59:24.0213785Z         dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:59:24.0214016Z         
2025-03-14T04:59:24.0214433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:59:24.0214970Z         dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:59:24.0215233Z         
2025-03-14T04:59:24.0215660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:59:24.0216184Z         dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:59:24.0216433Z         
2025-03-14T04:59:24.0216861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:59:24.0217413Z         getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:59:24.0217727Z         mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:59:24.0218057Z         getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:59:24.0218423Z         pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:59:24.0218680Z         
2025-03-14T04:59:24.0219114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:59:24.0220371Z         getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:59:24.0220712Z         mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:59:24.0221044Z         getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:59:24.0221385Z         pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:59:24.0221649Z         
2025-03-14T04:59:24.0222081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:59:24.0222608Z         exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:59:24.0222943Z         getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:59:24.0223291Z         pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:59:24.0224043Z         
2025-03-14T04:59:24.0224514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:59:24.0225043Z         exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:59:24.0225425Z         getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:59:24.0225796Z         pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:59:24.0226058Z         
2025-03-14T04:59:24.0226475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:59:24.0227000Z         mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:59:24.0227275Z         x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:59:24.0227517Z         
2025-03-14T04:59:24.0227918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:59:24.0228381Z         mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:59:24.0229157Z         y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:59:24.0229446Z         
2025-03-14T04:59:24.0229861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:59:24.0230351Z         mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:59:24.0230650Z         x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:59:24.0230906Z         
2025-03-14T04:59:24.0231300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:59:24.0231776Z         mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:59:24.0232062Z         y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:59:24.0233176Z         
2025-03-14T04:59:24.0233698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:59:24.0234307Z         pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:59:24.0234649Z         
2025-03-14T04:59:24.0235080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:59:24.0235645Z         predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320));  pred_boxes = None
2025-03-14T04:59:24.0235932Z         
2025-03-14T04:59:24.0236387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:59:24.0237009Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:59:24.0237380Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:59:24.0237673Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:59:24.0237982Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:59:24.0238311Z         getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:59:24.0238585Z         
2025-03-14T04:59:24.0238959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:59:24.0239521Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:59:24.0239864Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:59:24.0240108Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:59:24.0240487Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:59:24.0240843Z         getitem_26: "Sym(1230 - s0)" = size_3[0]
2025-03-14T04:59:24.0241093Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:59:24.0241365Z         
2025-03-14T04:59:24.0241803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:59:24.0242404Z         probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:59:24.0242727Z         
2025-03-14T04:59:24.0243162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:59:24.0243757Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:59:24.0244114Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:59:24.0244402Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:59:24.0244699Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:59:24.0245009Z         getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:59:24.0245272Z         
2025-03-14T04:59:24.0245825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:59:24.0246522Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:59:24.0246860Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:59:24.0247197Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:59:24.0247536Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:59:24.0247847Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:59:24.0248088Z         
2025-03-14T04:59:24.0248518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:59:24.0249028Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:59:24.0249255Z         
2025-03-14T04:59:24.0249396Z 
2025-03-14T04:59:24.0249487Z class GraphModule(torch.nn.Module):
2025-03-14T04:59:24.0250312Z     def forward(self, L_predictions_0_: "f32[3230, 81][81, 1]cpu", L_predictions_1_: "f32[3230, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"):
2025-03-14T04:59:24.0251121Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:59:24.0251430Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:59:24.0251825Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:59:24.0252239Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:59:24.0252645Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:59:24.0253048Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:59:24.0253331Z         
2025-03-14T04:59:24.0253736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:59:24.0254196Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:59:24.0254451Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:59:24.0254700Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:59:24.0254966Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:59:24.0255236Z         getitem_2: "Sym(1230 - s0)" = size_1[0]
2025-03-14T04:59:24.0255474Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:59:24.0255693Z         
2025-03-14T04:59:24.0256059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:59:24.0257002Z         proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:59:24.0257729Z         
2025-03-14T04:59:24.0259161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:59:24.0260083Z         deltas: "f32[3230, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:59:24.0260354Z         
2025-03-14T04:59:24.0260951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:59:24.0261474Z         boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:59:24.0261746Z         
2025-03-14T04:59:24.0262137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:59:24.0262620Z         getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:59:24.0262971Z         getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:24.0263280Z         widths: "f32[3230][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:59:24.0263534Z         
2025-03-14T04:59:24.0263923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:59:24.0264399Z         getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:59:24.0264685Z         getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:59:24.0264994Z         heights: "f32[3230][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:59:24.0265251Z         
2025-03-14T04:59:24.0265631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:59:24.0266098Z         getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:24.0266339Z         mul: "f32[3230][1]cpu" = 0.5 * widths
2025-03-14T04:59:24.0266585Z         ctr_x: "f32[3230][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:59:24.0266811Z         
2025-03-14T04:59:24.0267214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:59:24.0267707Z         getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:59:24.0267984Z         mul_1: "f32[3230][1]cpu" = 0.5 * heights
2025-03-14T04:59:24.0268235Z         ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:59:24.0268468Z         
2025-03-14T04:59:24.0268903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:59:24.0269402Z         getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:59:24.0269746Z         dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:59:24.0270012Z         
2025-03-14T04:59:24.0270387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:59:24.0270877Z         getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:59:24.0271194Z         dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:59:24.0271424Z         
2025-03-14T04:59:24.0271802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:59:24.0272285Z         getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:59:24.0272596Z         dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:59:24.0272821Z         
2025-03-14T04:59:24.0273196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:59:24.0273712Z         getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:59:24.0274044Z         dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:59:24.0274267Z         
2025-03-14T04:59:24.0274671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:59:24.0275185Z         dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:59:24.0275450Z         
2025-03-14T04:59:24.0275856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:59:24.0276368Z         dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:59:24.0276614Z         
2025-03-14T04:59:24.0277031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:59:24.0277550Z         getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:59:24.0277857Z         mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:59:24.0278178Z         getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:59:24.0278519Z         pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:59:24.0278772Z         
2025-03-14T04:59:24.0279198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:59:24.0279721Z         getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:59:24.0280025Z         mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:59:24.0280338Z         getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:59:24.0280667Z         pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:59:24.0280912Z         
2025-03-14T04:59:24.0281342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:59:24.0281830Z         exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:59:24.0282162Z         getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:59:24.0282507Z         pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:59:24.0282747Z         
2025-03-14T04:59:24.0283153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:59:24.0283640Z         exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:59:24.0283956Z         getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:59:24.0284292Z         pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:59:24.0284528Z         
2025-03-14T04:59:24.0284913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:59:24.0285362Z         mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:59:24.0285614Z         x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:59:24.0285845Z         
2025-03-14T04:59:24.0286228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:59:24.0286670Z         mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:59:24.0286920Z         y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:59:24.0287142Z         
2025-03-14T04:59:24.0287519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:59:24.0287998Z         mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:59:24.0288279Z         x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:59:24.0288518Z         
2025-03-14T04:59:24.0288896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:59:24.0289354Z         mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:59:24.0289632Z         y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:59:24.0289873Z         
2025-03-14T04:59:24.0290309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:59:24.0290903Z         pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:59:24.0291197Z         
2025-03-14T04:59:24.0291726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:59:24.0292326Z         predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320));  pred_boxes = None
2025-03-14T04:59:24.0292631Z         
2025-03-14T04:59:24.0293107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:59:24.0293754Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:59:24.0294163Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:59:24.0294461Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:59:24.0294775Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:59:24.0295117Z         getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:59:24.0295378Z         
2025-03-14T04:59:24.0295774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:59:24.0296336Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:59:24.0296694Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:59:24.0296939Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:59:24.0297308Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:59:24.0297665Z         getitem_26: "Sym(1230 - s0)" = size_3[0]
2025-03-14T04:59:24.0297915Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:59:24.0298129Z         
2025-03-14T04:59:24.0298550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:59:24.0299150Z         probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:59:24.0299479Z         
2025-03-14T04:59:24.0299925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:59:24.0300532Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:59:24.0300901Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:59:24.0301195Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:59:24.0301515Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:59:24.0301833Z         getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:59:24.0302099Z         
2025-03-14T04:59:24.0302656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:59:24.0303354Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:59:24.0303702Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:59:24.0304043Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:59:24.0304386Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:59:24.0304689Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:59:24.0304936Z         
2025-03-14T04:59:24.0305378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:59:24.0305901Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:59:24.0306134Z         
2025-03-14T04:59:24.0306264Z 
2025-03-14T04:59:24.0306360Z class GraphModule(torch.nn.Module):
2025-03-14T04:59:24.0308151Z     def forward(self, L_predictions_0_: "f32[3230, 81][81, 1]cpu", L_predictions_1_: "f32[3230, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"):
2025-03-14T04:59:24.0308943Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:59:24.0309183Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:59:24.0309482Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:59:24.0309892Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:59:24.0310270Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:59:24.0310644Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:59:24.0310924Z         
2025-03-14T04:59:24.0311302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:59:24.0311763Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:59:24.0312000Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:59:24.0312228Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:59:24.0312497Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:59:24.0312749Z         getitem_2: "Sym(1230 - s0)" = size_1[0]
2025-03-14T04:59:24.0312987Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:59:24.0313202Z         
2025-03-14T04:59:24.0313564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:59:24.0314488Z         proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:59:24.0315184Z         
2025-03-14T04:59:24.0315637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:59:24.0316220Z         deltas: "f32[3230, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:59:24.0316480Z         
2025-03-14T04:59:24.0316861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:59:24.0317369Z         boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:59:24.0317633Z         
2025-03-14T04:59:24.0318027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:59:24.0318508Z         getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:59:24.0318800Z         getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:24.0319109Z         widths: "f32[3230][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:59:24.0319360Z         
2025-03-14T04:59:24.0319747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:59:24.0320220Z         getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:59:24.0320496Z         getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:59:24.0320800Z         heights: "f32[3230][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:59:24.0321053Z         
2025-03-14T04:59:24.0321450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:59:24.0321925Z         getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:59:24.0322194Z         mul: "f32[3230][1]cpu" = 0.5 * widths
2025-03-14T04:59:24.0322443Z         ctr_x: "f32[3230][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:59:24.0322674Z         
2025-03-14T04:59:24.0323083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:59:24.0323571Z         getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:59:24.0323843Z         mul_1: "f32[3230][1]cpu" = 0.5 * heights
2025-03-14T04:59:24.0324096Z         ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:59:24.0324326Z         
2025-03-14T04:59:24.0324719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:59:24.0325218Z         getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:59:24.0325534Z         dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:59:24.0325761Z         
2025-03-14T04:59:24.0326132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:59:24.0326620Z         getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:59:24.0326928Z         dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:59:24.0327153Z         
2025-03-14T04:59:24.0327522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:59:24.0328009Z         getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:59:24.0328341Z         dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:59:24.0328562Z         
2025-03-14T04:59:24.0328942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:59:24.0329468Z         getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:59:24.0329806Z         dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:59:24.0330031Z         
2025-03-14T04:59:24.0330450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:59:24.0330973Z         dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:59:24.0331244Z         
2025-03-14T04:59:24.0331767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:59:24.0332327Z         dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:59:24.0332595Z         
2025-03-14T04:59:24.0333033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:59:24.0333571Z         getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:59:24.0333884Z         mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:59:24.0334232Z         getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:59:24.0334574Z         pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:59:24.0334830Z         
2025-03-14T04:59:24.0335281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:59:24.0335833Z         getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:59:24.0336141Z         mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:59:24.0336465Z         getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:59:24.0336797Z         pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:59:24.0337047Z         
2025-03-14T04:59:24.0337463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:59:24.0337966Z         exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:59:24.0338294Z         getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:59:24.0338634Z         pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:59:24.0338879Z         
2025-03-14T04:59:24.0339294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:59:24.0339792Z         exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:59:24.0340127Z         getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:59:24.0340477Z         pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:59:24.0340733Z         
2025-03-14T04:59:24.0341128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:59:24.0341626Z         mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:59:24.0341892Z         x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:59:24.0342121Z         
2025-03-14T04:59:24.0342509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:59:24.0342966Z         mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:59:24.0343221Z         y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:59:24.0343454Z         
2025-03-14T04:59:24.0343845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:59:24.0344302Z         mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:59:24.0344581Z         x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:59:24.0344811Z         
2025-03-14T04:59:24.0345188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:59:24.0345649Z         mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:59:24.0345922Z         y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:59:24.0346157Z         
2025-03-14T04:59:24.0346574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:59:24.0347153Z         pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:59:24.0347431Z         
2025-03-14T04:59:24.0347840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:59:24.0348410Z         predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320));  pred_boxes = None
2025-03-14T04:59:24.0348687Z         
2025-03-14T04:59:24.0349113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:59:24.0349706Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:59:24.0350056Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:59:24.0350332Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:59:24.0350624Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:59:24.0350931Z         getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:59:24.0351186Z         
2025-03-14T04:59:24.0351550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:59:24.0352089Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:59:24.0352424Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:59:24.0352653Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:59:24.0353002Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:59:24.0353342Z         getitem_26: "Sym(1230 - s0)" = size_3[0]
2025-03-14T04:59:24.0353577Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:59:24.0353786Z         
2025-03-14T04:59:24.0354209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:59:24.0354787Z         probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:59:24.0355098Z         
2025-03-14T04:59:24.0355524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:59:24.0356106Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:59:24.0356453Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:59:24.0356728Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:59:24.0357016Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:59:24.0357322Z         getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:59:24.0357570Z         
2025-03-14T04:59:24.0358103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:59:24.0358764Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:59:24.0359090Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:59:24.0359419Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:59:24.0359761Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:59:24.0360049Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:59:24.0360280Z         
2025-03-14T04:59:24.0360897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:59:24.0361451Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:59:24.0361683Z         
2025-03-14T04:59:26.1834451Z 
2025-03-14T04:59:26.1835316Z class GraphModule(torch.nn.Module):
2025-03-14T04:59:26.1835970Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T04:59:26.1836391Z         l_scores_0_ = L_scores_0_
2025-03-14T04:59:26.1836733Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:59:26.1836936Z         
2025-03-14T04:59:26.1837756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:59:26.1838617Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:59:26.1839054Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:59:26.1839432Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:59:26.1839806Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:59:26.1840141Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:59:26.1840441Z         
2025-03-14T04:59:26.1840919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:59:26.1841499Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:59:26.1841750Z         
2025-03-14T04:59:26.1842171Z 
2025-03-14T04:59:26.1842268Z class GraphModule(torch.nn.Module):
2025-03-14T04:59:26.1842594Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T04:59:26.1842908Z         l_scores_0_ = L_scores_0_
2025-03-14T04:59:26.1843110Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:59:26.1843304Z         
2025-03-14T04:59:26.1843888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:59:26.1844588Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:59:26.1844919Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:59:26.1845250Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:59:26.1845566Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:59:26.1845862Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:59:26.1846098Z         
2025-03-14T04:59:26.1846574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:59:26.1847110Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:59:26.1847340Z         
2025-03-14T04:59:41.2839837Z Compilation time (from dynamo_timed): 34.015178256
2025-03-14T04:59:41.2840324Z pass
2025-03-14T04:59:41.2844831Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:59:41.2846091Z TIMING: entire_frame_compile:34.01518 gc:0.03173 _recursive_pre_grad_passes:0.02747 async_compile.wait:8.36549 backend_compile:22.97429 _recursive_joint_graph_passes:0.44985 _recursive_post_grad_passes:0.09334 code_gen:11.37255 inductor_compile:12.85523 total_wall_time:34.01518
2025-03-14T04:59:41.2847341Z STATS: call_* op count: 611 | FakeTensorMode.__torch_dispatch__:18545 | FakeTensor.__torch_dispatch__:1849 | ProxyTorchDispatchMode.__torch_dispatch__:5936 | attempt fast:51 | slow no contiguity match:20 | fast is_contiguous:31
2025-03-14T04:59:41.2848017Z Dynamo produced 52 graphs covering 611 ops with 42 graph breaks (6 unique)
2025-03-14T04:59:46.3424558Z 
2025-03-14T04:59:51.9143685Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:59:51.9147178Z loading model: 0it [00:05, ?it/s]
2025-03-14T04:59:51.9151265Z cpu  eval  detectron2_fasterrcnn_r_50_fpn     
2025-03-14T05:00:05.7666021Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_50_fpn. Setting accuracy check to cosine
2025-03-14T05:00:05.7893168Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:00:14.4400107Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:00:22.1579143Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:00:32.2283352Z 
2025-03-14T05:00:32.2284056Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:32.2366480Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:00:32.2435269Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:00:32.2435917Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2436812Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2437773Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2438736Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2439699Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2440701Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2441685Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2442684Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2477672Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2478635Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2479437Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2480467Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2481363Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2482330Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2483145Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2483914Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2484763Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2485645Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2486496Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2487321Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2488128Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:32.2489028Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2489970Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2490882Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:32.2492034Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:32.2492974Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2493833Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2494702Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2495583Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2496492Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2497269Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2498074Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2498932Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2499815Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2500675Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2501477Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2502302Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2503209Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2504124Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2505001Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2505825Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2506688Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2507610Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2508505Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2509382Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2510228Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2511135Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2512066Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2512979Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2513866Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2514721Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2515604Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2516544Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2517452Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2518405Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2519196Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2520015Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2520900Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2521760Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2522572Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2523330Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2524161Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2525030Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2525909Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2526747Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2527547Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2528389Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2529328Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2530223Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2531090Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2532063Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:32.2533004Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2533921Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2534818Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:32.2535696Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:32.2536514Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2537343Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2538254Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2539122Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2540004Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2540811Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2541638Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2542536Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2543411Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2544245Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2545052Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2545890Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2546811Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2547677Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2548516Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2549315Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2550154Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2551044Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2551911Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2552777Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2553556Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2554435Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2555307Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2556150Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2556966Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2557742Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2558556Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2559421Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2560262Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2561262Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2562086Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2562921Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2563798Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2564646Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2565464Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2566244Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2567132Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2568062Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2568925Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2569774Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2570581Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2571483Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2572421Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2573318Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2574171Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2575021Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2575866Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2576757Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2577667Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2578513Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2579322Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2580157Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2581077Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2581955Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2582847Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2583657Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2584484Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2585369Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2586243Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2587074Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2587887Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:32.2588757Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2589712Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2590620Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:32.2591487Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:32.2592285Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2593110Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2593999Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2594834Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2595671Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2596463Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2597300Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2598173Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2599021Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2599856Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2600652Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2601484Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2602349Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2603194Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2604069Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2604884Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2605725Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2606635Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2607545Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2608410Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2609259Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2610121Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2611088Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2613129Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2614334Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2615411Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2616353Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2617272Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2618147Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2618998Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2619853Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2620685Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2621600Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2622483Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2623306Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2624089Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2624903Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2626129Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2626995Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2628030Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2628847Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2629685Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2630560Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2631412Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2632239Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2633040Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2633845Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2634759Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2635918Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2636759Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2637682Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2638539Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2639427Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2640279Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2641131Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2641954Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2642779Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2643662Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2644517Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2645632Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2646444Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2647519Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2648409Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2649319Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2650173Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2650986Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2651959Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2652940Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2653829Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2654667Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2655822Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2656688Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2657775Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2658675Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2659531Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2660358Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2661318Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2662225Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2663104Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2663948Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2664823Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2666001Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2666898Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2667869Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2668691Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2669474Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2670285Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2671190Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2672063Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2672903Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2673705Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:32.2674550Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2675717Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2676627Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:32.2677597Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:32.2678419Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2679251Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2680170Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2681020Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2681850Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2682647Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2683462Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2684311Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2685425Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2686320Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2687755Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2688613Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2689515Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2690414Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2691288Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2692234Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:32.2693107Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2693986Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2694862Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.2696014Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.2696831Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:32.2697678Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2698574Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2699460Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.2700305Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.2701138Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:32.2701977Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.2702902Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.2703774Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.2704617Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.2705331Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T05:00:32.2705852Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T05:00:32.2706355Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T05:00:32.2706855Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T05:00:32.2707354Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T05:00:32.2707850Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T05:00:32.2708347Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T05:00:32.2708871Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T05:00:32.2709378Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T05:00:32.2709881Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T05:00:32.2710384Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T05:00:32.2710882Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T05:00:32.2711382Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T05:00:32.2711885Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T05:00:32.2712389Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T05:00:32.2712898Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T05:00:32.2713536Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:00:32.2714320Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:00:32.2715125Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:00:32.2715902Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:00:32.2716737Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:00:32.2717495Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:00:32.2718206Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:00:32.2718980Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:00:32.2719823Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:00:32.2720635Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:00:32.2721426Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:00:32.2721927Z         
2025-03-14T05:00:32.2722358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2723311Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.2724026Z         
2025-03-14T05:00:32.2724416Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2727819Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2730003Z         
2025-03-14T05:00:32.2730435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:00:32.2730981Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:00:32.2731277Z         
2025-03-14T05:00:32.2731918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:00:32.2732700Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:00:32.2733109Z         
2025-03-14T05:00:32.2733494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2734399Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.2735082Z         
2025-03-14T05:00:32.2736170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2738738Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2740808Z         
2025-03-14T05:00:32.2741233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2741744Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:00:32.2742015Z         
2025-03-14T05:00:32.2742374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2743226Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.2743875Z         
2025-03-14T05:00:32.2744246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2746891Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2749219Z         
2025-03-14T05:00:32.2749645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2750226Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:00:32.2750503Z         
2025-03-14T05:00:32.2750859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2751672Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.2752317Z         
2025-03-14T05:00:32.2752681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2756198Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2759184Z         
2025-03-14T05:00:32.2759566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2760406Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:32.2761138Z         
2025-03-14T05:00:32.2761504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2763985Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2766785Z         
2025-03-14T05:00:32.2767212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.2767813Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:00:32.2768147Z         
2025-03-14T05:00:32.2768568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2769128Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:00:32.2769438Z         
2025-03-14T05:00:32.2769827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2770771Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.2771581Z         
2025-03-14T05:00:32.2771996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2774374Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2776467Z         
2025-03-14T05:00:32.2776856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2777363Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:00:32.2777641Z         
2025-03-14T05:00:32.2777992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2778826Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.2779466Z         
2025-03-14T05:00:32.2779834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2782064Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2784004Z         
2025-03-14T05:00:32.2784368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2784846Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:00:32.2785102Z         
2025-03-14T05:00:32.2785440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2786259Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.2786916Z         
2025-03-14T05:00:32.2787278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2789529Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2791580Z         
2025-03-14T05:00:32.2791968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.2792491Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:00:32.2792773Z         
2025-03-14T05:00:32.2793163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2793685Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:00:32.2793963Z         
2025-03-14T05:00:32.2794322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2795167Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.2795795Z         
2025-03-14T05:00:32.2796178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2798529Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2800602Z         
2025-03-14T05:00:32.2801011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2801523Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:00:32.2801793Z         
2025-03-14T05:00:32.2802144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2802994Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.2803632Z         
2025-03-14T05:00:32.2803998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2806352Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2808485Z         
2025-03-14T05:00:32.2808910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2809462Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:00:32.2809758Z         
2025-03-14T05:00:32.2810137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2811082Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.2811862Z         
2025-03-14T05:00:32.2812279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2814638Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2816674Z         
2025-03-14T05:00:32.2817057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.2817570Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:00:32.2817850Z         
2025-03-14T05:00:32.2818237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2818757Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:00:32.2819039Z         
2025-03-14T05:00:32.2819391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2820261Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.2820903Z         
2025-03-14T05:00:32.2821279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2823522Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2825440Z         
2025-03-14T05:00:32.2825815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2826326Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:00:32.2826599Z         
2025-03-14T05:00:32.2826934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2827800Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.2828435Z         
2025-03-14T05:00:32.2828786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2830919Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2832841Z         
2025-03-14T05:00:32.2833229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2833712Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:00:32.2833997Z         
2025-03-14T05:00:32.2834346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2835198Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.2835853Z         
2025-03-14T05:00:32.2836222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2838474Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2840519Z         
2025-03-14T05:00:32.2840893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2841765Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:32.2842432Z         
2025-03-14T05:00:32.2842799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2845113Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2847350Z         
2025-03-14T05:00:32.2847757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.2848297Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:00:32.2848594Z         
2025-03-14T05:00:32.2849026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2849584Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:00:32.2849881Z         
2025-03-14T05:00:32.2850252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2851141Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.2851887Z         
2025-03-14T05:00:32.2852319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2854686Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2856830Z         
2025-03-14T05:00:32.2857240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2857803Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:00:32.2858100Z         
2025-03-14T05:00:32.2858476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2859392Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.2860078Z         
2025-03-14T05:00:32.2860464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2862986Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2865173Z         
2025-03-14T05:00:32.2865588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2866132Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:00:32.2866425Z         
2025-03-14T05:00:32.2866795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2867692Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.2868383Z         
2025-03-14T05:00:32.2868770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2871088Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2873147Z         
2025-03-14T05:00:32.2873518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.2874008Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:00:32.2874277Z         
2025-03-14T05:00:32.2874637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2875128Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:00:32.2875399Z         
2025-03-14T05:00:32.2875733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2876537Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.2877138Z         
2025-03-14T05:00:32.2877483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2879649Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2881572Z         
2025-03-14T05:00:32.2881941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2882421Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:00:32.2882687Z         
2025-03-14T05:00:32.2883022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2883826Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.2884435Z         
2025-03-14T05:00:32.2884781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2886986Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2889004Z         
2025-03-14T05:00:32.2889392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2889905Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:00:32.2890182Z         
2025-03-14T05:00:32.2890533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2892877Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.2893656Z         
2025-03-14T05:00:32.2894484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2897017Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2899222Z         
2025-03-14T05:00:32.2899619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.2900151Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:00:32.2900444Z         
2025-03-14T05:00:32.2900835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2901358Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:00:32.2901637Z         
2025-03-14T05:00:32.2901995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2902876Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.2903563Z         
2025-03-14T05:00:32.2903961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2906733Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2908951Z         
2025-03-14T05:00:32.2909350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2909850Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:00:32.2910117Z         
2025-03-14T05:00:32.2910457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2911272Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.2911919Z         
2025-03-14T05:00:32.2912269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2914399Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2916678Z         
2025-03-14T05:00:32.2917082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2917589Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:00:32.2917866Z         
2025-03-14T05:00:32.2918241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2919073Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.2919706Z         
2025-03-14T05:00:32.2920055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2922325Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2924298Z         
2025-03-14T05:00:32.2924684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.2925220Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:00:32.2925510Z         
2025-03-14T05:00:32.2925912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2926485Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:00:32.2926790Z         
2025-03-14T05:00:32.2927163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2928054Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.2928728Z         
2025-03-14T05:00:32.2929114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2931628Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2933897Z         
2025-03-14T05:00:32.2934300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2934822Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:00:32.2935100Z         
2025-03-14T05:00:32.2935454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2936338Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.2936981Z         
2025-03-14T05:00:32.2937347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2939604Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2941632Z         
2025-03-14T05:00:32.2942028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2942530Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:00:32.2942803Z         
2025-03-14T05:00:32.2943156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2943994Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.2944629Z         
2025-03-14T05:00:32.2944976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2947133Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2949050Z         
2025-03-14T05:00:32.2949399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2950251Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:32.2950856Z         
2025-03-14T05:00:32.2951226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2953549Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2955667Z         
2025-03-14T05:00:32.2956049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.2956528Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:00:32.2956784Z         
2025-03-14T05:00:32.2957140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2957624Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:00:32.2957876Z         
2025-03-14T05:00:32.2958208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2959023Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.2959668Z         
2025-03-14T05:00:32.2960028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2962494Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2964543Z         
2025-03-14T05:00:32.2964939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2965439Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:00:32.2965712Z         
2025-03-14T05:00:32.2966061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2966900Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.2967544Z         
2025-03-14T05:00:32.2967913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2970286Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2972596Z         
2025-03-14T05:00:32.2972995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2973495Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:00:32.2973761Z         
2025-03-14T05:00:32.2974117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2974931Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.2975542Z         
2025-03-14T05:00:32.2975895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2978168Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2980076Z         
2025-03-14T05:00:32.2980439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.2980945Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:00:32.2981227Z         
2025-03-14T05:00:32.2981614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2982154Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:00:32.2982426Z         
2025-03-14T05:00:32.2982775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2983582Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.2984175Z         
2025-03-14T05:00:32.2984529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2987381Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2989385Z         
2025-03-14T05:00:32.2989773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2990280Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:00:32.2990545Z         
2025-03-14T05:00:32.2990895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2991752Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.2992390Z         
2025-03-14T05:00:32.2992756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.2995070Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.2997075Z         
2025-03-14T05:00:32.2997458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.2997951Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:00:32.2998210Z         
2025-03-14T05:00:32.2998556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.2999409Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3000079Z         
2025-03-14T05:00:32.3000478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3002728Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3004737Z         
2025-03-14T05:00:32.3005124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3005634Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:00:32.3005913Z         
2025-03-14T05:00:32.3006300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3006833Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:00:32.3007107Z         
2025-03-14T05:00:32.3007453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3008311Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3008936Z         
2025-03-14T05:00:32.3009302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3011749Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3013953Z         
2025-03-14T05:00:32.3014368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3014909Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:00:32.3015197Z         
2025-03-14T05:00:32.3015572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3016492Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3017167Z         
2025-03-14T05:00:32.3017552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3019943Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3022067Z         
2025-03-14T05:00:32.3022450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3022923Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:00:32.3023177Z         
2025-03-14T05:00:32.3023535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3024350Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3042265Z         
2025-03-14T05:00:32.3042907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3045129Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3047296Z         
2025-03-14T05:00:32.3047737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3048422Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:00:32.3048734Z         
2025-03-14T05:00:32.3049165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3049720Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:00:32.3050019Z         
2025-03-14T05:00:32.3050401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3051314Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3052869Z         
2025-03-14T05:00:32.3053292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3055638Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3057705Z         
2025-03-14T05:00:32.3058133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3058655Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:00:32.3058928Z         
2025-03-14T05:00:32.3059324Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3060191Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3061070Z         
2025-03-14T05:00:32.3061474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3063751Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3065778Z         
2025-03-14T05:00:32.3066148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3066624Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:00:32.3066880Z         
2025-03-14T05:00:32.3067215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3068009Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3068617Z         
2025-03-14T05:00:32.3068967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3071192Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3073254Z         
2025-03-14T05:00:32.3073636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3074141Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:00:32.3074406Z         
2025-03-14T05:00:32.3074773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3075253Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:00:32.3075518Z         
2025-03-14T05:00:32.3075853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3076647Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3077241Z         
2025-03-14T05:00:32.3077599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3079831Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3081872Z         
2025-03-14T05:00:32.3082262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3082774Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:00:32.3083050Z         
2025-03-14T05:00:32.3083413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3084266Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3084915Z         
2025-03-14T05:00:32.3085305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3087717Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3089934Z         
2025-03-14T05:00:32.3090341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3090880Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:00:32.3091173Z         
2025-03-14T05:00:32.3091626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3092570Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3093248Z         
2025-03-14T05:00:32.3093640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3095969Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3098053Z         
2025-03-14T05:00:32.3098457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3098986Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:00:32.3099272Z         
2025-03-14T05:00:32.3099677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3100209Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:00:32.3100497Z         
2025-03-14T05:00:32.3100880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3101766Z         x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3102449Z         
2025-03-14T05:00:32.3102856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3105271Z         x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3107391Z         
2025-03-14T05:00:32.3107809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3108347Z         out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:00:32.3108630Z         
2025-03-14T05:00:32.3109010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3109898Z         x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3110586Z         
2025-03-14T05:00:32.3110963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3113313Z         x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3115457Z         
2025-03-14T05:00:32.3115863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3116409Z         out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:00:32.3116692Z         
2025-03-14T05:00:32.3117066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3117988Z         x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3118663Z         
2025-03-14T05:00:32.3119045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3121327Z         x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3123326Z         
2025-03-14T05:00:32.3123690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3124533Z         x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:32.3125202Z         
2025-03-14T05:00:32.3125562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3127867Z         x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3130231Z         
2025-03-14T05:00:32.3130634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3131161Z         x_93 += x_95;  out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93;  x_93 = x_95 = None
2025-03-14T05:00:32.3131505Z         
2025-03-14T05:00:32.3131940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3132485Z         out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:00:32.3132804Z         
2025-03-14T05:00:32.3133153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3134002Z         x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3134631Z         
2025-03-14T05:00:32.3134998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3137139Z         x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3139044Z         
2025-03-14T05:00:32.3139411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3139897Z         out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:00:32.3140141Z         
2025-03-14T05:00:32.3140486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3141299Z         x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3141901Z         
2025-03-14T05:00:32.3142253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3144413Z         x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3146337Z         
2025-03-14T05:00:32.3146720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3147232Z         out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99);  x_99 = None
2025-03-14T05:00:32.3147484Z         
2025-03-14T05:00:32.3147833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3148631Z         x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3149238Z         
2025-03-14T05:00:32.3149586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3151704Z         x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3153634Z         
2025-03-14T05:00:32.3153997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3154485Z         x_101 += out_55;  out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101;  x_101 = out_55 = None
2025-03-14T05:00:32.3154751Z         
2025-03-14T05:00:32.3155117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3155596Z         out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:00:32.3155854Z         
2025-03-14T05:00:32.3156184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3156963Z         x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3157556Z         
2025-03-14T05:00:32.3157904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3160045Z         x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3162085Z         
2025-03-14T05:00:32.3162466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3162952Z         out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:00:32.3163229Z         
2025-03-14T05:00:32.3163593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3164440Z         x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3165086Z         
2025-03-14T05:00:32.3165457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3167734Z         x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3169914Z         
2025-03-14T05:00:32.3170318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3170851Z         out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105);  x_105 = None
2025-03-14T05:00:32.3171133Z         
2025-03-14T05:00:32.3171562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3172524Z         x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3173181Z         
2025-03-14T05:00:32.3173545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3175843Z         x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3177884Z         
2025-03-14T05:00:32.3178262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3178778Z         x_107 += out_59;  out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107;  x_107 = out_59 = None
2025-03-14T05:00:32.3179060Z         
2025-03-14T05:00:32.3179450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3179930Z         out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:00:32.3180191Z         
2025-03-14T05:00:32.3180522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3181397Z         x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T05:00:32.3182075Z         
2025-03-14T05:00:32.3182404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3183281Z         x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T05:00:32.3183944Z         
2025-03-14T05:00:32.3184435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:00:32.3185171Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest');  x_108 = None
2025-03-14T05:00:32.3185553Z         
2025-03-14T05:00:32.3185881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3186882Z         x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T05:00:32.3189101Z         
2025-03-14T05:00:32.3189616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:00:32.3190290Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features;  x_110 = top_down_features = None
2025-03-14T05:00:32.3190597Z         
2025-03-14T05:00:32.3190929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3191877Z         x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T05:00:32.3192582Z         
2025-03-14T05:00:32.3193090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:00:32.3193896Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T05:00:32.3194347Z         
2025-03-14T05:00:32.3194702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3195652Z         x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T05:00:32.3196391Z         
2025-03-14T05:00:32.3196839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:00:32.3197490Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1;  x_112 = top_down_features_1 = None
2025-03-14T05:00:32.3197832Z         
2025-03-14T05:00:32.3198202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3199142Z         x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T05:00:32.3199897Z         
2025-03-14T05:00:32.3200434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:00:32.3201800Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T05:00:32.3202276Z         
2025-03-14T05:00:32.3202660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3203593Z         x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T05:00:32.3204322Z         
2025-03-14T05:00:32.3204771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:00:32.3205461Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2;  x_114 = top_down_features_2 = None
2025-03-14T05:00:32.3205805Z         
2025-03-14T05:00:32.3206179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3207178Z         x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T05:00:32.3209069Z         
2025-03-14T05:00:32.3209888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T05:00:32.3210603Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T05:00:32.3211008Z         
2025-03-14T05:00:32.3211907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3212749Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:00:32.3213049Z         
2025-03-14T05:00:32.3213484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3214041Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:00:32.3214327Z         
2025-03-14T05:00:32.3214917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3216022Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:00:32.3216346Z         
2025-03-14T05:00:32.3216786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3217544Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:00:32.3217837Z         
2025-03-14T05:00:32.3218576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:32.3219406Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:00:32.3219891Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:00:32.3220355Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:00:32.3220625Z         
2025-03-14T05:00:32.3221090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:32.3221659Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:00:32.3221930Z         
2025-03-14T05:00:32.3222404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:32.3223079Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:00:32.3223347Z         
2025-03-14T05:00:32.3223897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:32.3224615Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:00:32.3224991Z         
2025-03-14T05:00:32.3225870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:32.3226516Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:00:32.3227472Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:00:32.3228105Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:00:32.3228412Z         x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:00:32.3228661Z         
2025-03-14T05:00:32.3229201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3229848Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:00:32.3230120Z         
2025-03-14T05:00:32.3230505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3231002Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:00:32.3231263Z         
2025-03-14T05:00:32.3231793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3232468Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:00:32.3232735Z         
2025-03-14T05:00:32.3233118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3233608Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:00:32.3233862Z         
2025-03-14T05:00:32.3234325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:32.3234954Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:00:32.3235613Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:00:32.3235922Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:00:32.3236175Z         
2025-03-14T05:00:32.3236607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:32.3237130Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:00:32.3237382Z         
2025-03-14T05:00:32.3237806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:32.3238316Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:00:32.3238565Z         
2025-03-14T05:00:32.3239057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:32.3239740Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:00:32.3240076Z         
2025-03-14T05:00:32.3240600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:32.3241213Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:00:32.3241839Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:00:32.3242447Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:00:32.3242758Z         x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:00:32.3242999Z         
2025-03-14T05:00:32.3243528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3244170Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:00:32.3244441Z         
2025-03-14T05:00:32.3244822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3245341Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:00:32.3245600Z         
2025-03-14T05:00:32.3246123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3246797Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:00:32.3247069Z         
2025-03-14T05:00:32.3247453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3247945Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:00:32.3248216Z         
2025-03-14T05:00:32.3248688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:32.3249331Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:00:32.3249698Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:00:32.3249985Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:00:32.3250233Z         
2025-03-14T05:00:32.3250663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:32.3251192Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:00:32.3251518Z         
2025-03-14T05:00:32.3251968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:32.3252537Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:00:32.3252835Z         
2025-03-14T05:00:32.3253319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:32.3253993Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:00:32.3254319Z         
2025-03-14T05:00:32.3254814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:32.3255410Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:00:32.3256013Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:00:32.3256606Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:00:32.3256902Z         x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T05:00:32.3257139Z         
2025-03-14T05:00:32.3257657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3258296Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:00:32.3258563Z         
2025-03-14T05:00:32.3258946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3259446Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:00:32.3259707Z         
2025-03-14T05:00:32.3260227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3261039Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:00:32.3261312Z         
2025-03-14T05:00:32.3261698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3262194Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:00:32.3262458Z         
2025-03-14T05:00:32.3262924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:32.3263551Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:00:32.3263906Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:00:32.3264178Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:00:32.3264409Z         
2025-03-14T05:00:32.3264831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:32.3265347Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:00:32.3265594Z         
2025-03-14T05:00:32.3266008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:32.3266575Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:00:32.3266825Z         
2025-03-14T05:00:32.3267303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:32.3268016Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:00:32.3268354Z         
2025-03-14T05:00:32.3268866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:32.3269470Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:00:32.3270084Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:00:32.3270692Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:00:32.3270996Z         x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T05:00:32.3271237Z         
2025-03-14T05:00:32.3271771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3272417Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:00:32.3272684Z         
2025-03-14T05:00:32.3273086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3273570Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:00:32.3273826Z         
2025-03-14T05:00:32.3274376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3275019Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:00:32.3275282Z         
2025-03-14T05:00:32.3275668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3276149Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:00:32.3276412Z         
2025-03-14T05:00:32.3276875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:32.3277497Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:00:32.3277844Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:00:32.3278115Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:00:32.3278352Z         
2025-03-14T05:00:32.3278773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:32.3279286Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:00:32.3279529Z         
2025-03-14T05:00:32.3279955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:32.3280455Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:00:32.3280691Z         
2025-03-14T05:00:32.3281155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:32.3281830Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:00:32.3282153Z         
2025-03-14T05:00:32.3282645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:32.3283221Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:00:32.3283820Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:00:32.3284406Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:00:32.3284698Z         x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T05:00:32.3284931Z         
2025-03-14T05:00:32.3285312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:32.3285788Z         tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32);  x_116 = None
2025-03-14T05:00:32.3286096Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32);  x_117 = None
2025-03-14T05:00:32.3286396Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32);  x_118 = None
2025-03-14T05:00:32.3286696Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32);  x_119 = None
2025-03-14T05:00:32.3286991Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32);  x_120 = None
2025-03-14T05:00:32.3287247Z         
2025-03-14T05:00:32.3287586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3288404Z         x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_115 = None
2025-03-14T05:00:32.3289031Z         
2025-03-14T05:00:32.3289394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:32.3289931Z         x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False);  x_121 = None
2025-03-14T05:00:32.3290255Z         
2025-03-14T05:00:32.3290739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:32.3291813Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:32.3292533Z         
2025-03-14T05:00:32.3293011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:32.3293986Z         x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_122 = None
2025-03-14T05:00:32.3294671Z         
2025-03-14T05:00:32.3295051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3295863Z         x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_113 = None
2025-03-14T05:00:32.3296480Z         
2025-03-14T05:00:32.3296844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:32.3297045Z         x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False);  x_123 = None
2025-03-14T05:00:32.3297118Z         
2025-03-14T05:00:32.3297501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:32.3298041Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:32.3298112Z         
2025-03-14T05:00:32.3298475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:32.3299012Z         x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_124 = None
2025-03-14T05:00:32.3299103Z         
2025-03-14T05:00:32.3299371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3299861Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_111 = None
2025-03-14T05:00:32.3299935Z         
2025-03-14T05:00:32.3300212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:32.3300409Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False);  x_125 = None
2025-03-14T05:00:32.3300472Z         
2025-03-14T05:00:32.3300859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:32.3301389Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:32.3301460Z         
2025-03-14T05:00:32.3301827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:32.3302364Z         x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_126 = None
2025-03-14T05:00:32.3302454Z         
2025-03-14T05:00:32.3302733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3303226Z         x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_109 = None
2025-03-14T05:00:32.3303289Z         
2025-03-14T05:00:32.3303576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:32.3303760Z         x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False);  x_127 = None
2025-03-14T05:00:32.3303832Z         
2025-03-14T05:00:32.3304218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:32.3304737Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:32.3304799Z         
2025-03-14T05:00:32.3305172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:32.3305700Z         x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_128 = None
2025-03-14T05:00:32.3305786Z         
2025-03-14T05:00:32.3306056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3306848Z         x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:00:32.3306922Z         
2025-03-14T05:00:32.3307196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:32.3307382Z         x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False);  x_129 = None
2025-03-14T05:00:32.3307447Z         
2025-03-14T05:00:32.3307840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:32.3308742Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:00:32.3308815Z         
2025-03-14T05:00:32.3309191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:32.3310063Z         x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:00:32.3310136Z         
2025-03-14T05:00:32.3310483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:00:32.3310662Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:00:32.3310811Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:00:32.3310988Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:00:32.3311132Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:00:32.3311295Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:00:32.3311434Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:00:32.3311603Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:00:32.3311737Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:00:32.3311885Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:00:32.3312030Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:00:32.3312097Z         
2025-03-14T05:00:32.3312522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:00:32.3312704Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304);  x_131 = None
2025-03-14T05:00:32.3312892Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:00:32.3313068Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:00:32.3313235Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152);  x_132 = None
2025-03-14T05:00:32.3313408Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:00:32.3313585Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:00:32.3313732Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76);  x_133 = None
2025-03-14T05:00:32.3313899Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:00:32.3314066Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:00:32.3314230Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38);  x_134 = None
2025-03-14T05:00:32.3314388Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:00:32.3314578Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:00:32.3314730Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19);  x_135 = None
2025-03-14T05:00:32.3314898Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:00:32.3315058Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:00:32.3315128Z         
2025-03-14T05:00:32.3315534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:32.3315741Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:00:32.3315806Z         
2025-03-14T05:00:32.3316248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:32.3316408Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:00:32.3316553Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:00:32.3316698Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:00:32.3316759Z         
2025-03-14T05:00:32.3317150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:32.3317323Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:00:32.3317415Z         
2025-03-14T05:00:32.3317743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:32.3317897Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:00:32.3317962Z         
2025-03-14T05:00:32.3318295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:32.3318432Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:32.3318572Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3318733Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:00:32.3318806Z         
2025-03-14T05:00:32.3319136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:32.3319287Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:32.3319411Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:32.3319576Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:00:32.3319641Z         
2025-03-14T05:00:32.3319963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:32.3320107Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3320203Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:00:32.3320331Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:00:32.3320428Z         
2025-03-14T05:00:32.3320760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:32.3320919Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:32.3321010Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:00:32.3321150Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:00:32.3321211Z         
2025-03-14T05:00:32.3321566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:32.3321734Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:32.3321858Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:00:32.3321922Z         
2025-03-14T05:00:32.3322235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:32.3322385Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:32.3322504Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:00:32.3322564Z         
2025-03-14T05:00:32.3322871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:32.3323019Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:32.3323139Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:00:32.3323202Z         
2025-03-14T05:00:32.3323526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:32.3323718Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:32.3323828Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:00:32.3323896Z         
2025-03-14T05:00:32.3324231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:32.3324378Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:32.3324440Z         
2025-03-14T05:00:32.3324779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:32.3324912Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:32.3324981Z         
2025-03-14T05:00:32.3325321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:32.3325465Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:32.3325589Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:00:32.3325745Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:32.3325898Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:00:32.3325967Z         
2025-03-14T05:00:32.3326313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:32.3326475Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:32.3326610Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:00:32.3326767Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:32.3326899Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:00:32.3326968Z         
2025-03-14T05:00:32.3327292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:32.3327413Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:32.3327572Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:32.3327712Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:00:32.3327772Z         
2025-03-14T05:00:32.3328108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:32.3328223Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:32.3328398Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:32.3328534Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:00:32.3328604Z         
2025-03-14T05:00:32.3328918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:32.3329046Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:32.3329166Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:32.3329237Z         
2025-03-14T05:00:32.3329556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:32.3329658Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:32.3329773Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:32.3329845Z         
2025-03-14T05:00:32.3330167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:32.3330298Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:32.3330432Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:32.3330508Z         
2025-03-14T05:00:32.3330828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:32.3330955Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:32.3331088Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:32.3331159Z         
2025-03-14T05:00:32.3331615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:32.3331816Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:32.3331912Z         
2025-03-14T05:00:32.3332262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:32.3332461Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:00:32.3332529Z         
2025-03-14T05:00:32.3332951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:32.3333147Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:00:32.3333220Z         
2025-03-14T05:00:32.3333631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:32.3333854Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:00:32.3333917Z         
2025-03-14T05:00:32.3334367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:32.3334522Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:00:32.3334681Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:00:32.3334823Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:00:32.3334892Z         
2025-03-14T05:00:32.3335273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:32.3335453Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:00:32.3335533Z         
2025-03-14T05:00:32.3335860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:32.3336009Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:00:32.3336080Z         
2025-03-14T05:00:32.3336400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:32.3336542Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:00:32.3336670Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3336834Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:00:32.3336897Z         
2025-03-14T05:00:32.3337232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:32.3337363Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:00:32.3337496Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:00:32.3337654Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:00:32.3337724Z         
2025-03-14T05:00:32.3338040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:32.3338199Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3338294Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:00:32.3338436Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:00:32.3338517Z         
2025-03-14T05:00:32.3338855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:32.3339009Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:00:32.3339109Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:00:32.3339238Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:00:32.3339308Z         
2025-03-14T05:00:32.3339621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:32.3339778Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:32.3339901Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:00:32.3339965Z         
2025-03-14T05:00:32.3340272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:32.3340423Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:32.3340541Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:00:32.3340604Z         
2025-03-14T05:00:32.3340913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:32.3341066Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:32.3341184Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:00:32.3341265Z         
2025-03-14T05:00:32.3341575Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:32.3341765Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:00:32.3341885Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:00:32.3341944Z         
2025-03-14T05:00:32.3342289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:32.3342431Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:00:32.3342499Z         
2025-03-14T05:00:32.3342832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:32.3342980Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:00:32.3343039Z         
2025-03-14T05:00:32.3343399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:32.3343531Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:00:32.3343659Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:00:32.3343809Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:00:32.3343974Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:00:32.3344034Z         
2025-03-14T05:00:32.3344378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:32.3344528Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:00:32.3344664Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:00:32.3344821Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:00:32.3344959Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:00:32.3345024Z         
2025-03-14T05:00:32.3345351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:32.3345470Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:00:32.3345634Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:00:32.3345777Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:00:32.3345837Z         
2025-03-14T05:00:32.3346174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:32.3346284Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:00:32.3346458Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:00:32.3346591Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:00:32.3346661Z         
2025-03-14T05:00:32.3346971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:32.3347094Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:00:32.3347212Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:00:32.3347282Z         
2025-03-14T05:00:32.3347586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:32.3347684Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:00:32.3347799Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:00:32.3347866Z         
2025-03-14T05:00:32.3348173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:32.3348295Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:00:32.3348428Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:00:32.3348496Z         
2025-03-14T05:00:32.3348798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:32.3348916Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:00:32.3349045Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:00:32.3349111Z         
2025-03-14T05:00:32.3349462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:32.3349684Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:00:32.3349746Z         
2025-03-14T05:00:32.3350088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:32.3350289Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:00:32.3350361Z         
2025-03-14T05:00:32.3350746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:32.3350933Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:00:32.3350993Z         
2025-03-14T05:00:32.3351410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:32.3351612Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:00:32.3351684Z         
2025-03-14T05:00:32.3352117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:32.3352274Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:00:32.3352429Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:00:32.3352565Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:00:32.3352634Z         
2025-03-14T05:00:32.3353005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:32.3353198Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:00:32.3353260Z         
2025-03-14T05:00:32.3353575Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:32.3353715Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:00:32.3353783Z         
2025-03-14T05:00:32.3354092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:32.3354227Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:00:32.3354347Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3354500Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:00:32.3354564Z         
2025-03-14T05:00:32.3354887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:32.3355006Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:00:32.3355129Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:00:32.3355279Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:00:32.3355346Z         
2025-03-14T05:00:32.3355671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:32.3355800Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3355889Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:00:32.3356044Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:00:32.3356104Z         
2025-03-14T05:00:32.3356434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:32.3356581Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:00:32.3356680Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:00:32.3356809Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:00:32.3356880Z         
2025-03-14T05:00:32.3357183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:32.3357345Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:32.3357464Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:00:32.3357537Z         
2025-03-14T05:00:32.3357840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:32.3358000Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:32.3358112Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:00:32.3358183Z         
2025-03-14T05:00:32.3358488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:32.3358644Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:32.3358779Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:00:32.3358839Z         
2025-03-14T05:00:32.3359150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:32.3359331Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:00:32.3359446Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:00:32.3359505Z         
2025-03-14T05:00:32.3359842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:32.3359979Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:00:32.3360047Z         
2025-03-14T05:00:32.3360374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:32.3360678Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:00:32.3360751Z         
2025-03-14T05:00:32.3361105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:32.3361237Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:00:32.3361369Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:00:32.3361570Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:00:32.3361718Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:00:32.3361781Z         
2025-03-14T05:00:32.3362177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:32.3362330Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:00:32.3362460Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:00:32.3362607Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:00:32.3362753Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:00:32.3362814Z         
2025-03-14T05:00:32.3363147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:32.3363260Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:00:32.3363426Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:00:32.3363557Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:00:32.3363636Z         
2025-03-14T05:00:32.3363960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:32.3364077Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:00:32.3364237Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:00:32.3364375Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:00:32.3364434Z         
2025-03-14T05:00:32.3364748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:32.3364878Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:00:32.3365001Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:00:32.3365061Z         
2025-03-14T05:00:32.3365466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:32.3365560Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:00:32.3365676Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:00:32.3365736Z         
2025-03-14T05:00:32.3366047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:32.3366159Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:00:32.3366301Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:00:32.3366361Z         
2025-03-14T05:00:32.3366668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:32.3366785Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:00:32.3366912Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:00:32.3366983Z         
2025-03-14T05:00:32.3367343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:32.3367541Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:00:32.3367620Z         
2025-03-14T05:00:32.3367973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:32.3368134Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:00:32.3368203Z         
2025-03-14T05:00:32.3368583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:32.3368766Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:00:32.3368829Z         
2025-03-14T05:00:32.3369252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:32.3369457Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:00:32.3369524Z         
2025-03-14T05:00:32.3369968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:32.3370125Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:00:32.3370274Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:00:32.3370419Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:00:32.3370479Z         
2025-03-14T05:00:32.3370870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:32.3371096Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:00:32.3371168Z         
2025-03-14T05:00:32.3374316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:32.3374545Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:00:32.3374616Z         
2025-03-14T05:00:32.3374956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:32.3375100Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:00:32.3375268Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3375455Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:00:32.3375536Z         
2025-03-14T05:00:32.3375865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:32.3375999Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:00:32.3376119Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:00:32.3376277Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:00:32.3376340Z         
2025-03-14T05:00:32.3376732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:32.3376864Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3376978Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:00:32.3377121Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:00:32.3377209Z         
2025-03-14T05:00:32.3377534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:32.3377681Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:00:32.3377784Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:00:32.3377915Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:00:32.3377985Z         
2025-03-14T05:00:32.3378308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:32.3378473Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:32.3378587Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:00:32.3378658Z         
2025-03-14T05:00:32.3378962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:32.3379130Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:32.3379239Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:00:32.3379304Z         
2025-03-14T05:00:32.3379600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:32.3379755Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:32.3379892Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:00:32.3379965Z         
2025-03-14T05:00:32.3380277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:32.3380472Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:00:32.3380583Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:00:32.3380653Z         
2025-03-14T05:00:32.3381000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:32.3381148Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:00:32.3381212Z         
2025-03-14T05:00:32.3381561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:32.3381703Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:00:32.3381772Z         
2025-03-14T05:00:32.3382126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:32.3382265Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:00:32.3382389Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:00:32.3382569Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:00:32.3382718Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:00:32.3382796Z         
2025-03-14T05:00:32.3383174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:32.3383310Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:00:32.3383440Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:00:32.3383592Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:00:32.3383735Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:00:32.3383796Z         
2025-03-14T05:00:32.3384147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:32.3384263Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:00:32.3384434Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:00:32.3384566Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:00:32.3384635Z         
2025-03-14T05:00:32.3384980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:32.3385100Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:00:32.3385267Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:00:32.3385405Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:00:32.3385484Z         
2025-03-14T05:00:32.3385804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:32.3385904Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:00:32.3386025Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:00:32.3386094Z         
2025-03-14T05:00:32.3386490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:32.3386591Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:00:32.3386714Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:00:32.3386777Z         
2025-03-14T05:00:32.3387094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:32.3387211Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:00:32.3387351Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:00:32.3387413Z         
2025-03-14T05:00:32.3387725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:32.3387836Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:00:32.3387972Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:00:32.3388034Z         
2025-03-14T05:00:32.3388409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:32.3388602Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:00:32.3388690Z         
2025-03-14T05:00:32.3389056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:32.3389228Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:00:32.3389288Z         
2025-03-14T05:00:32.3389684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:32.3389860Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:00:32.3389928Z         
2025-03-14T05:00:32.3390333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:32.3390553Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:00:32.3390621Z         
2025-03-14T05:00:32.3391066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:32.3391215Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:00:32.3391360Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:00:32.3391498Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:00:32.3391558Z         
2025-03-14T05:00:32.3391927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:32.3392117Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:00:32.3392185Z         
2025-03-14T05:00:32.3392488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:32.3392631Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:00:32.3392691Z         
2025-03-14T05:00:32.3393004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:32.3393126Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:00:32.3393256Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3393399Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:00:32.3393467Z         
2025-03-14T05:00:32.3393776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:32.3393903Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:00:32.3394020Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:00:32.3394174Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:00:32.3394235Z         
2025-03-14T05:00:32.3394576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:32.3394697Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3394811Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:00:32.3394957Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:00:32.3395028Z         
2025-03-14T05:00:32.3395355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:32.3395505Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:00:32.3395590Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:00:32.3395724Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:00:32.3395783Z         
2025-03-14T05:00:32.3396088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:32.3396240Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:32.3396355Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:00:32.3396415Z         
2025-03-14T05:00:32.3396725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:32.3396870Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:32.3396984Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:00:32.3397043Z         
2025-03-14T05:00:32.3397344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:32.3397493Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:32.3397626Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:00:32.3397693Z         
2025-03-14T05:00:32.3397995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:32.3398180Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:00:32.3398285Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:00:32.3398353Z         
2025-03-14T05:00:32.3398687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:32.3398830Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:00:32.3398893Z         
2025-03-14T05:00:32.3399227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:32.3399357Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:00:32.3399424Z         
2025-03-14T05:00:32.3399764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:32.3399903Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:00:32.3400038Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:00:32.3400198Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:00:32.3400330Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:00:32.3400415Z         
2025-03-14T05:00:32.3400777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:32.3400916Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:00:32.3401035Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:00:32.3401189Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:00:32.3401319Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:00:32.3401388Z         
2025-03-14T05:00:32.3401720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:32.3401841Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:00:32.3402000Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:00:32.3402134Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:00:32.3402197Z         
2025-03-14T05:00:32.3402535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:32.3402644Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:00:32.3402814Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:00:32.3402940Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:00:32.3403027Z         
2025-03-14T05:00:32.3403341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:32.3403444Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:00:32.3403554Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:00:32.3403620Z         
2025-03-14T05:00:32.3403930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:32.3404028Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:00:32.3404137Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:00:32.3404205Z         
2025-03-14T05:00:32.3404512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:32.3404633Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:00:32.3404773Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:00:32.3404834Z         
2025-03-14T05:00:32.3405143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:32.3405253Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:00:32.3405387Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:00:32.3405448Z         
2025-03-14T05:00:32.3405816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:32.3406005Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:00:32.3406093Z         
2025-03-14T05:00:32.3406456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:32.3406630Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:00:32.3406695Z         
2025-03-14T05:00:32.3407102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:32.3407281Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:00:32.3407353Z         
2025-03-14T05:00:32.3407863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:00:32.3408015Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:00:32.3408079Z         
2025-03-14T05:00:32.3408422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3408567Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:00:32.3408638Z         
2025-03-14T05:00:32.3409109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:32.3409245Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:00:32.3409377Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:00:32.3409510Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:00:32.3409576Z         
2025-03-14T05:00:32.3410101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:32.3410246Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:32.3410507Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:00:32.3410573Z         
2025-03-14T05:00:32.3411097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:32.3411271Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:32.3411342Z         
2025-03-14T05:00:32.3411733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3411881Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:00:32.3411949Z         
2025-03-14T05:00:32.3412435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:32.3412586Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:00:32.3412697Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:00:32.3412828Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:00:32.3412919Z         
2025-03-14T05:00:32.3414059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:32.3414225Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:32.3414475Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:00:32.3414541Z         
2025-03-14T05:00:32.3415027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:32.3415200Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:32.3415273Z         
2025-03-14T05:00:32.3415580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3415719Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:00:32.3415785Z         
2025-03-14T05:00:32.3416245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:32.3416365Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:00:32.3416482Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:00:32.3416602Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:00:32.3416694Z         
2025-03-14T05:00:32.3417176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:32.3417320Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:32.3417565Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:00:32.3417636Z         
2025-03-14T05:00:32.3418112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:32.3418286Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:32.3418352Z         
2025-03-14T05:00:32.3418664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3418791Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:00:32.3418862Z         
2025-03-14T05:00:32.3419308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:32.3419430Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:00:32.3419542Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:00:32.3419682Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:00:32.3419752Z         
2025-03-14T05:00:32.3420240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:32.3420422Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:32.3420665Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:00:32.3420737Z         
2025-03-14T05:00:32.3421208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:32.3421381Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:32.3421451Z         
2025-03-14T05:00:32.3421749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3421870Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:00:32.3421936Z         
2025-03-14T05:00:32.3422357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:32.3422471Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:00:32.3422571Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:00:32.3422690Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:00:32.3422751Z         
2025-03-14T05:00:32.3423202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:32.3423386Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:00:32.3423622Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:00:32.3423682Z         
2025-03-14T05:00:32.3424133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:32.3424289Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:32.3424358Z         
2025-03-14T05:00:32.3424646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3424777Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:00:32.3424838Z         
2025-03-14T05:00:32.3425124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:32.3425503Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:00:32.3425563Z         
2025-03-14T05:00:32.3425863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:32.3426322Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:00:32.3426407Z         
2025-03-14T05:00:32.3426699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:32.3426904Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:00:32.3426964Z         
2025-03-14T05:00:32.3427363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:00:32.3427500Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:00:32.3427569Z         
2025-03-14T05:00:32.3427862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:32.3428013Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:00:32.3428073Z         
2025-03-14T05:00:32.3428452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:00:32.3428583Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:00:32.3428649Z         
2025-03-14T05:00:32.3429131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:00:32.3429284Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:00:32.3429422Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:32.3429580Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:00:32.3429708Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:00:32.3429778Z         
2025-03-14T05:00:32.3430138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:00:32.3430259Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:00:32.3430320Z         
2025-03-14T05:00:32.3430890Z 
2025-03-14T05:00:32.3430982Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:32.3498386Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:00:32.3499063Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:00:32.3499504Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3500019Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3500545Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3500979Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3501429Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3501885Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3502431Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3502902Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3503357Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3503769Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3504179Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3504650Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3505094Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3505546Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3505985Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3506386Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3506849Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3507298Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3507736Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3508169Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3508617Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:32.3509148Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3509616Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3510139Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:32.3510611Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:32.3511015Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3511473Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3511916Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3512358Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3512791Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3513205Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3513699Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3514148Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3514599Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3515038Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3515450Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3515922Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3516394Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3516843Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3517305Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3517710Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3518176Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3518615Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3519040Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3519435Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3519771Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3520171Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3520557Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3520956Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3521324Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3521661Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3522065Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3522454Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3522834Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3523208Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3523553Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3523997Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3524423Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3524854Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3525258Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3525657Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3526101Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3526546Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3526976Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3527409Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3527811Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3528299Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3528799Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3529293Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3529781Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3530251Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:32.3530813Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3531427Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3532019Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:32.3532562Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:32.3533019Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3533522Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3533986Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3534410Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3534837Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3535211Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3535680Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3536114Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3536538Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3536952Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3537333Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3537790Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3538258Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3538681Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3539132Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3539522Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3539969Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3540407Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3540833Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3541243Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3541630Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3542076Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3542533Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3542955Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3543361Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3543749Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3544190Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3544637Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3545059Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3545485Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3545870Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3546355Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3546796Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3547211Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3547629Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3548021Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3548460Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3548910Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3549321Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3549756Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3550133Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3550587Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3551030Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3551451Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3551863Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3552237Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3552707Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3553175Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3553602Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3554015Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3554392Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3554836Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3555270Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3555690Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3556092Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3556491Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3556942Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3557376Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3557796Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3558202Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3558605Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:32.3559059Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3559532Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3559976Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:32.3560451Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:32.3560949Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3561399Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3561828Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3562207Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3562586Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3562932Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3563328Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3563791Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3564204Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3564631Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3565016Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3565471Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3565916Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3566332Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3566767Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3567185Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3567673Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3568115Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3568540Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3568960Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3569346Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3569804Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3570258Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3570695Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3571141Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3571578Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3572089Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3572614Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3573076Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3573496Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3573897Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3574348Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3574818Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3575245Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3575655Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3576039Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3576486Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3576935Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3577360Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3577770Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3578188Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3578626Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3579070Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3579483Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3579896Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3580280Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3580716Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3581173Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3581596Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3582045Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3582423Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3582873Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3583316Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3583734Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3584148Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3584526Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3584971Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3585427Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3585847Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3586259Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3586640Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3587103Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3587550Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3587971Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3588393Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3588811Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3589263Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3589698Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3590124Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3590532Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3590919Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3591355Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3591794Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3592232Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3592640Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3593023Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3593462Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3593911Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3594329Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3594740Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3595149Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3595592Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3596110Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3596509Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3596883Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3597221Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3597628Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3598029Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3598404Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3598804Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3599229Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:32.3599695Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3600142Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3600581Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:32.3601016Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:32.3601401Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3601846Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3602279Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3602717Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3603140Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3603523Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3603969Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3604407Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3604839Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3605249Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3605641Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3606092Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3606551Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3606977Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3607397Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3607798Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:32.3608258Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3608717Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3609171Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.3609594Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.3610031Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:32.3610485Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3610948Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3611478Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.3611987Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.3612448Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:32.3612981Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.3613521Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.3614050Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.3614539Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.3614834Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T05:00:32.3615121Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T05:00:32.3615400Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T05:00:32.3615677Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T05:00:32.3615970Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T05:00:32.3616240Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T05:00:32.3616521Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T05:00:32.3616783Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T05:00:32.3617097Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T05:00:32.3617340Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T05:00:32.3617611Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T05:00:32.3617865Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T05:00:32.3618125Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T05:00:32.3618366Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T05:00:32.3618619Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T05:00:32.3618856Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T05:00:32.3619269Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:00:32.3619677Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:00:32.3620083Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:00:32.3620490Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:00:32.3620886Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:00:32.3621232Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:00:32.3621544Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:00:32.3621919Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:00:32.3622280Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:00:32.3622639Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:00:32.3622982Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:00:32.3623052Z         
2025-03-14T05:00:32.3623344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3623919Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3623993Z         
2025-03-14T05:00:32.3624276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3626068Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3626145Z         
2025-03-14T05:00:32.3626449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:00:32.3626592Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:00:32.3626662Z         
2025-03-14T05:00:32.3627032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:00:32.3627281Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:00:32.3627341Z         
2025-03-14T05:00:32.3627609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3628120Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3628184Z         
2025-03-14T05:00:32.3628466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3630253Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3630324Z         
2025-03-14T05:00:32.3630612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3630767Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:00:32.3630835Z         
2025-03-14T05:00:32.3631088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3631631Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3631693Z         
2025-03-14T05:00:32.3631975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3633776Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3633848Z         
2025-03-14T05:00:32.3634149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3634287Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:00:32.3634352Z         
2025-03-14T05:00:32.3634620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3635135Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3635194Z         
2025-03-14T05:00:32.3635472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3637277Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3637340Z         
2025-03-14T05:00:32.3637608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3638104Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:32.3638204Z         
2025-03-14T05:00:32.3638467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3640442Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3640521Z         
2025-03-14T05:00:32.3640831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3641010Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:00:32.3641074Z         
2025-03-14T05:00:32.3641381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3641558Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:00:32.3641637Z         
2025-03-14T05:00:32.3641888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3642387Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3642457Z         
2025-03-14T05:00:32.3642722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3644554Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3644617Z         
2025-03-14T05:00:32.3644911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3645075Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:00:32.3645152Z         
2025-03-14T05:00:32.3645410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3645904Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3645973Z         
2025-03-14T05:00:32.3646239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3648138Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3648233Z         
2025-03-14T05:00:32.3648545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3648711Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:00:32.3648776Z         
2025-03-14T05:00:32.3649071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3649631Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3649709Z         
2025-03-14T05:00:32.3650003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3652092Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3652200Z         
2025-03-14T05:00:32.3652555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3652741Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:00:32.3652815Z         
2025-03-14T05:00:32.3653155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3653305Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:00:32.3653377Z         
2025-03-14T05:00:32.3653644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3654152Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3654220Z         
2025-03-14T05:00:32.3654500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3656299Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3656376Z         
2025-03-14T05:00:32.3656670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3656808Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:00:32.3656876Z         
2025-03-14T05:00:32.3657123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3657624Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3657691Z         
2025-03-14T05:00:32.3657952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3659888Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3659968Z         
2025-03-14T05:00:32.3660260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3660401Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:00:32.3660463Z         
2025-03-14T05:00:32.3660837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3661348Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3661418Z         
2025-03-14T05:00:32.3661683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3663524Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3663633Z         
2025-03-14T05:00:32.3663917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3664075Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:00:32.3664136Z         
2025-03-14T05:00:32.3664419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3664567Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:00:32.3664633Z         
2025-03-14T05:00:32.3664875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3665378Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3665441Z         
2025-03-14T05:00:32.3665706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3667550Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3667616Z         
2025-03-14T05:00:32.3667906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3668046Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:00:32.3668114Z         
2025-03-14T05:00:32.3668361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3668861Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3668939Z         
2025-03-14T05:00:32.3669213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3670990Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3671052Z         
2025-03-14T05:00:32.3671338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3671474Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:00:32.3671541Z         
2025-03-14T05:00:32.3671778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3672289Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3672371Z         
2025-03-14T05:00:32.3672642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3674427Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3674495Z         
2025-03-14T05:00:32.3674740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3675239Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:32.3675300Z         
2025-03-14T05:00:32.3675580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3677386Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3677455Z         
2025-03-14T05:00:32.3677732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3677876Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:00:32.3677943Z         
2025-03-14T05:00:32.3678216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3678384Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:00:32.3678445Z         
2025-03-14T05:00:32.3678698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3679215Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3679283Z         
2025-03-14T05:00:32.3679552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3681333Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3681406Z         
2025-03-14T05:00:32.3681690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3681840Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:00:32.3681923Z         
2025-03-14T05:00:32.3682182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3682681Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3682750Z         
2025-03-14T05:00:32.3683012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3684835Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3684908Z         
2025-03-14T05:00:32.3685209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3685355Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:00:32.3685432Z         
2025-03-14T05:00:32.3685703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3686207Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3686274Z         
2025-03-14T05:00:32.3686544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3688497Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3688578Z         
2025-03-14T05:00:32.3688894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3689084Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:00:32.3689158Z         
2025-03-14T05:00:32.3689477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3689647Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:00:32.3689713Z         
2025-03-14T05:00:32.3690004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3690541Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3690618Z         
2025-03-14T05:00:32.3690918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3693268Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3693371Z         
2025-03-14T05:00:32.3693696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3693858Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:00:32.3693934Z         
2025-03-14T05:00:32.3694214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3694742Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3694818Z         
2025-03-14T05:00:32.3695103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3697099Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3697194Z         
2025-03-14T05:00:32.3697497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3697648Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:00:32.3697712Z         
2025-03-14T05:00:32.3697984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3698508Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3698582Z         
2025-03-14T05:00:32.3698859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3700798Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3700895Z         
2025-03-14T05:00:32.3701191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3701363Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:00:32.3701432Z         
2025-03-14T05:00:32.3701741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3701901Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:00:32.3701977Z         
2025-03-14T05:00:32.3702242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3702766Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3702841Z         
2025-03-14T05:00:32.3703122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3705041Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3705130Z         
2025-03-14T05:00:32.3705431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3705586Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:00:32.3705648Z         
2025-03-14T05:00:32.3705912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3706433Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3706549Z         
2025-03-14T05:00:32.3706826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3708776Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3708872Z         
2025-03-14T05:00:32.3709177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3709332Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:00:32.3709395Z         
2025-03-14T05:00:32.3709661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3710188Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3710261Z         
2025-03-14T05:00:32.3710541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3712469Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3712544Z         
2025-03-14T05:00:32.3712838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3713003Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:00:32.3713068Z         
2025-03-14T05:00:32.3713370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3713524Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:00:32.3713614Z         
2025-03-14T05:00:32.3713874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3714423Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3714488Z         
2025-03-14T05:00:32.3714776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3716681Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3716747Z         
2025-03-14T05:00:32.3717052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3717193Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:00:32.3717264Z         
2025-03-14T05:00:32.3717526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3718066Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3718137Z         
2025-03-14T05:00:32.3718414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3720320Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3720393Z         
2025-03-14T05:00:32.3720706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3720853Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:00:32.3720917Z         
2025-03-14T05:00:32.3721203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3721700Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3721768Z         
2025-03-14T05:00:32.3722029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3723822Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3723891Z         
2025-03-14T05:00:32.3724140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3724643Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:32.3724721Z         
2025-03-14T05:00:32.3724988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3726840Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3726905Z         
2025-03-14T05:00:32.3727187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3727338Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:00:32.3727406Z         
2025-03-14T05:00:32.3727687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3727849Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:00:32.3727911Z         
2025-03-14T05:00:32.3728179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3728658Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3728726Z         
2025-03-14T05:00:32.3728990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3730836Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3730909Z         
2025-03-14T05:00:32.3731238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3731434Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:00:32.3731509Z         
2025-03-14T05:00:32.3731805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3732352Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3732423Z         
2025-03-14T05:00:32.3732718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3734595Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3734693Z         
2025-03-14T05:00:32.3734991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3735138Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:00:32.3735209Z         
2025-03-14T05:00:32.3735465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3735976Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3736039Z         
2025-03-14T05:00:32.3736311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3738143Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3738230Z         
2025-03-14T05:00:32.3738518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3738660Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:00:32.3738726Z         
2025-03-14T05:00:32.3739005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3739152Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:00:32.3739214Z         
2025-03-14T05:00:32.3739468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3739947Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3740015Z         
2025-03-14T05:00:32.3740277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3742109Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3742195Z         
2025-03-14T05:00:32.3742475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3742612Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:00:32.3742674Z         
2025-03-14T05:00:32.3742928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3743415Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3743482Z         
2025-03-14T05:00:32.3743743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3745506Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3745590Z         
2025-03-14T05:00:32.3745865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3745998Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:00:32.3746058Z         
2025-03-14T05:00:32.3746307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3746778Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3746845Z         
2025-03-14T05:00:32.3747106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3748916Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3749002Z         
2025-03-14T05:00:32.3749285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3749437Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:00:32.3749518Z         
2025-03-14T05:00:32.3749795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3749936Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:00:32.3749993Z         
2025-03-14T05:00:32.3750241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3750708Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3750789Z         
2025-03-14T05:00:32.3751046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3752804Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3752870Z         
2025-03-14T05:00:32.3753143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3753276Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:00:32.3753335Z         
2025-03-14T05:00:32.3753578Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3754060Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3754144Z         
2025-03-14T05:00:32.3754402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3756222Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3756294Z         
2025-03-14T05:00:32.3756581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3756718Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:00:32.3756780Z         
2025-03-14T05:00:32.3757032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3757513Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3757599Z         
2025-03-14T05:00:32.3757853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3759600Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3759674Z         
2025-03-14T05:00:32.3759951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3760100Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:00:32.3760160Z         
2025-03-14T05:00:32.3760467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3760729Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:00:32.3760846Z         
2025-03-14T05:00:32.3761096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3761602Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3761670Z         
2025-03-14T05:00:32.3761925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3763765Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3763837Z         
2025-03-14T05:00:32.3764124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3764287Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:00:32.3764350Z         
2025-03-14T05:00:32.3764603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3765078Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3765146Z         
2025-03-14T05:00:32.3765404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3767229Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3767300Z         
2025-03-14T05:00:32.3767582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3767734Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:00:32.3767795Z         
2025-03-14T05:00:32.3768078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3768593Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3768664Z         
2025-03-14T05:00:32.3768940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3770849Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3770944Z         
2025-03-14T05:00:32.3771233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3771434Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:00:32.3771504Z         
2025-03-14T05:00:32.3771807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3771951Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:00:32.3772026Z         
2025-03-14T05:00:32.3772287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3772805Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3772868Z         
2025-03-14T05:00:32.3773142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3774969Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3775051Z         
2025-03-14T05:00:32.3775343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3775471Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:00:32.3775541Z         
2025-03-14T05:00:32.3775787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3776279Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3776349Z         
2025-03-14T05:00:32.3776611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3778424Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3778504Z         
2025-03-14T05:00:32.3778795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3778932Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:00:32.3778993Z         
2025-03-14T05:00:32.3779247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3779738Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3779807Z         
2025-03-14T05:00:32.3780068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3781888Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3781977Z         
2025-03-14T05:00:32.3782257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3782406Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:00:32.3782468Z         
2025-03-14T05:00:32.3782757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3782897Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:00:32.3782964Z         
2025-03-14T05:00:32.3783212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3783694Z         x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3783755Z         
2025-03-14T05:00:32.3784027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3785865Z         x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3785928Z         
2025-03-14T05:00:32.3786217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3786349Z         out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:00:32.3786419Z         
2025-03-14T05:00:32.3786663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3787164Z         x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3787226Z         
2025-03-14T05:00:32.3787497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3789327Z         x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3789392Z         
2025-03-14T05:00:32.3789684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3789815Z         out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:00:32.3789885Z         
2025-03-14T05:00:32.3790131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3790633Z         x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3790717Z         
2025-03-14T05:00:32.3790979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3792793Z         x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3792856Z         
2025-03-14T05:00:32.3793113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3793608Z         x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:32.3793670Z         
2025-03-14T05:00:32.3793952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3795841Z         x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3795930Z         
2025-03-14T05:00:32.3796242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3796379Z         x_93 += x_95;  out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93;  x_93 = x_95 = None
2025-03-14T05:00:32.3796449Z         
2025-03-14T05:00:32.3796731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3796872Z         out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:00:32.3796933Z         
2025-03-14T05:00:32.3797190Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3797663Z         x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3797752Z         
2025-03-14T05:00:32.3798016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3799821Z         x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3799892Z         
2025-03-14T05:00:32.3800173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3800311Z         out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:00:32.3800372Z         
2025-03-14T05:00:32.3800638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3801138Z         x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3801226Z         
2025-03-14T05:00:32.3801491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3803289Z         x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3803362Z         
2025-03-14T05:00:32.3803645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3803785Z         out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99);  x_99 = None
2025-03-14T05:00:32.3803848Z         
2025-03-14T05:00:32.3804110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3804622Z         x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3804695Z         
2025-03-14T05:00:32.3804958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3806843Z         x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3806917Z         
2025-03-14T05:00:32.3807210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3807394Z         x_101 += out_55;  out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101;  x_101 = out_55 = None
2025-03-14T05:00:32.3807460Z         
2025-03-14T05:00:32.3807765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3807934Z         out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:00:32.3808021Z         
2025-03-14T05:00:32.3808291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3808809Z         x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.3808881Z         
2025-03-14T05:00:32.3809159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3811089Z         x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3811226Z         
2025-03-14T05:00:32.3811599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3811764Z         out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:00:32.3811832Z         
2025-03-14T05:00:32.3812114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3812651Z         x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.3812734Z         
2025-03-14T05:00:32.3813006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3814933Z         x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3815028Z         
2025-03-14T05:00:32.3815350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3815503Z         out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105);  x_105 = None
2025-03-14T05:00:32.3815566Z         
2025-03-14T05:00:32.3815833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3816351Z         x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.3816424Z         
2025-03-14T05:00:32.3816696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.3818603Z         x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.3818694Z         
2025-03-14T05:00:32.3818989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.3819149Z         x_107 += out_59;  out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107;  x_107 = out_59 = None
2025-03-14T05:00:32.3819213Z         
2025-03-14T05:00:32.3819512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.3819655Z         out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:00:32.3819728Z         
2025-03-14T05:00:32.3819986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3820595Z         x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T05:00:32.3820667Z         
2025-03-14T05:00:32.3820925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3821521Z         x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T05:00:32.3821598Z         
2025-03-14T05:00:32.3822033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:00:32.3822301Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest');  x_108 = None
2025-03-14T05:00:32.3822368Z         
2025-03-14T05:00:32.3822613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3823180Z         x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T05:00:32.3823243Z         
2025-03-14T05:00:32.3823594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:00:32.3823784Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features;  x_110 = top_down_features = None
2025-03-14T05:00:32.3823853Z         
2025-03-14T05:00:32.3824096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3824664Z         x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T05:00:32.3824750Z         
2025-03-14T05:00:32.3825151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:00:32.3825479Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T05:00:32.3825542Z         
2025-03-14T05:00:32.3825796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3826369Z         x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T05:00:32.3826440Z         
2025-03-14T05:00:32.3826782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:00:32.3826997Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1;  x_112 = top_down_features_1 = None
2025-03-14T05:00:32.3827058Z         
2025-03-14T05:00:32.3827312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3827912Z         x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T05:00:32.3827990Z         
2025-03-14T05:00:32.3828405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:00:32.3828728Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T05:00:32.3828796Z         
2025-03-14T05:00:32.3829042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3829623Z         x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T05:00:32.3829687Z         
2025-03-14T05:00:32.3830038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:00:32.3830246Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2;  x_114 = top_down_features_2 = None
2025-03-14T05:00:32.3830315Z         
2025-03-14T05:00:32.3830560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3831187Z         x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T05:00:32.3831277Z         
2025-03-14T05:00:32.3831638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T05:00:32.3831852Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T05:00:32.3831915Z         
2025-03-14T05:00:32.3832356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3832508Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:00:32.3832580Z         
2025-03-14T05:00:32.3832873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3833019Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:00:32.3833082Z         
2025-03-14T05:00:32.3833521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3833670Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:00:32.3833741Z         
2025-03-14T05:00:32.3834044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3834192Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:00:32.3834282Z         
2025-03-14T05:00:32.3834674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:32.3834855Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:00:32.3834960Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:00:32.3835081Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:00:32.3835151Z         
2025-03-14T05:00:32.3835489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:32.3835622Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:00:32.3835683Z         
2025-03-14T05:00:32.3836017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:32.3836150Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:00:32.3836217Z         
2025-03-14T05:00:32.3836589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:32.3836802Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:00:32.3836860Z         
2025-03-14T05:00:32.3837280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:32.3837421Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:00:32.3837841Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:00:32.3837968Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:00:32.3838084Z         x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:00:32.3838151Z         
2025-03-14T05:00:32.3838580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3838732Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:00:32.3838796Z         
2025-03-14T05:00:32.3839093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3839233Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:00:32.3839301Z         
2025-03-14T05:00:32.3839729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3839877Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:00:32.3839938Z         
2025-03-14T05:00:32.3840251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3840388Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:00:32.3840477Z         
2025-03-14T05:00:32.3840864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:32.3841068Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:00:32.3841169Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:00:32.3841299Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:00:32.3841362Z         
2025-03-14T05:00:32.3841691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:32.3841817Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:00:32.3841888Z         
2025-03-14T05:00:32.3842212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:32.3842343Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:00:32.3842404Z         
2025-03-14T05:00:32.3842797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:32.3843019Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:00:32.3843089Z         
2025-03-14T05:00:32.3843521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:32.3843682Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:00:32.3844144Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:00:32.3844280Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:00:32.3844414Z         x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:00:32.3844480Z         
2025-03-14T05:00:32.3844969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3845130Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:00:32.3845206Z         
2025-03-14T05:00:32.3845531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3845688Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:00:32.3845756Z         
2025-03-14T05:00:32.3846243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3846415Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:00:32.3846493Z         
2025-03-14T05:00:32.3846813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3846985Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:00:32.3847052Z         
2025-03-14T05:00:32.3847491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:32.3847707Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:00:32.3847823Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:00:32.3847954Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:00:32.3848029Z         
2025-03-14T05:00:32.3848394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:32.3848540Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:00:32.3848609Z         
2025-03-14T05:00:32.3848980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:32.3849111Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:00:32.3849190Z         
2025-03-14T05:00:32.3849636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:32.3849886Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:00:32.3849953Z         
2025-03-14T05:00:32.3850441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:32.3850600Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:00:32.3851071Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:00:32.3851211Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:00:32.3851336Z         x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T05:00:32.3851481Z         
2025-03-14T05:00:32.3851990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3852161Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:00:32.3852230Z         
2025-03-14T05:00:32.3852574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3852737Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:00:32.3852815Z         
2025-03-14T05:00:32.3853306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3853489Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:00:32.3853558Z         
2025-03-14T05:00:32.3853893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3854057Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:00:32.3854150Z         
2025-03-14T05:00:32.3854562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:32.3854779Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:00:32.3854887Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:00:32.3855023Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:00:32.3855093Z         
2025-03-14T05:00:32.3855466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:32.3855604Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:00:32.3855681Z         
2025-03-14T05:00:32.3856046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:32.3856185Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:00:32.3856254Z         
2025-03-14T05:00:32.3856684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:32.3856915Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:00:32.3856989Z         
2025-03-14T05:00:32.3857450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:32.3857616Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:00:32.3858078Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:00:32.3858208Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:00:32.3858340Z         x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T05:00:32.3858410Z         
2025-03-14T05:00:32.3858894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3859053Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:00:32.3859128Z         
2025-03-14T05:00:32.3859458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3859608Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:00:32.3859674Z         
2025-03-14T05:00:32.3860158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.3860336Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:00:32.3860411Z         
2025-03-14T05:00:32.3860838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3861067Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:00:32.3861140Z         
2025-03-14T05:00:32.3861556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:32.3861746Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:00:32.3861850Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:00:32.3861968Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:00:32.3862034Z         
2025-03-14T05:00:32.3862363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:32.3862496Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:00:32.3862555Z         
2025-03-14T05:00:32.3862893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:32.3863010Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:00:32.3863079Z         
2025-03-14T05:00:32.3863463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:32.3863679Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:00:32.3863764Z         
2025-03-14T05:00:32.3864181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:32.3864306Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:00:32.3864726Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:00:32.3864852Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:00:32.3864966Z         x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T05:00:32.3865033Z         
2025-03-14T05:00:32.3865331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:32.3865471Z         tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32);  x_116 = None
2025-03-14T05:00:32.3865598Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32);  x_117 = None
2025-03-14T05:00:32.3865728Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32);  x_118 = None
2025-03-14T05:00:32.3865849Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32);  x_119 = None
2025-03-14T05:00:32.3865973Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32);  x_120 = None
2025-03-14T05:00:32.3866034Z         
2025-03-14T05:00:32.3866298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3866832Z         x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_115 = None
2025-03-14T05:00:32.3866921Z         
2025-03-14T05:00:32.3867212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:32.3867420Z         x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False);  x_121 = None
2025-03-14T05:00:32.3867482Z         
2025-03-14T05:00:32.3867871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:32.3868390Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:32.3868461Z         
2025-03-14T05:00:32.3868819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:32.3869342Z         x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_122 = None
2025-03-14T05:00:32.3869412Z         
2025-03-14T05:00:32.3869663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3870152Z         x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_113 = None
2025-03-14T05:00:32.3870230Z         
2025-03-14T05:00:32.3870510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:32.3870704Z         x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False);  x_123 = None
2025-03-14T05:00:32.3870772Z         
2025-03-14T05:00:32.3871146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:32.3871663Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:32.3871726Z         
2025-03-14T05:00:32.3872090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:32.3872603Z         x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_124 = None
2025-03-14T05:00:32.3872671Z         
2025-03-14T05:00:32.3872942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3873418Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_111 = None
2025-03-14T05:00:32.3873505Z         
2025-03-14T05:00:32.3873807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:32.3874005Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False);  x_125 = None
2025-03-14T05:00:32.3874069Z         
2025-03-14T05:00:32.3874463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:32.3874979Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:32.3875049Z         
2025-03-14T05:00:32.3875402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:32.3875904Z         x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_126 = None
2025-03-14T05:00:32.3875965Z         
2025-03-14T05:00:32.3876223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3876698Z         x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_109 = None
2025-03-14T05:00:32.3876784Z         
2025-03-14T05:00:32.3877060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:32.3877249Z         x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False);  x_127 = None
2025-03-14T05:00:32.3877320Z         
2025-03-14T05:00:32.3877705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:32.3878231Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:32.3878297Z         
2025-03-14T05:00:32.3878670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:32.3879159Z         x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_128 = None
2025-03-14T05:00:32.3879229Z         
2025-03-14T05:00:32.3879491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.3880271Z         x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:00:32.3880356Z         
2025-03-14T05:00:32.3880626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:32.3880802Z         x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False);  x_129 = None
2025-03-14T05:00:32.3880861Z         
2025-03-14T05:00:32.3881236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:32.3882083Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:00:32.3882150Z         
2025-03-14T05:00:32.3882500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:32.3883318Z         x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:00:32.3883411Z         
2025-03-14T05:00:32.3883749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:00:32.3883919Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:00:32.3884059Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:00:32.3884226Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:00:32.3884368Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:00:32.3884528Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:00:32.3884664Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:00:32.3884816Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:00:32.3884946Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:00:32.3885096Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:00:32.3885225Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:00:32.3885293Z         
2025-03-14T05:00:32.3885730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:00:32.3885930Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304);  x_131 = None
2025-03-14T05:00:32.3886130Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:00:32.3886317Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:00:32.3886478Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152);  x_132 = None
2025-03-14T05:00:32.3886658Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:00:32.3886828Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:00:32.3886983Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76);  x_133 = None
2025-03-14T05:00:32.3887150Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:00:32.3887324Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:00:32.3887469Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38);  x_134 = None
2025-03-14T05:00:32.3887627Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:00:32.3887798Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:00:32.3887936Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19);  x_135 = None
2025-03-14T05:00:32.3888101Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:00:32.3888280Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:00:32.3888348Z         
2025-03-14T05:00:32.3888750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:32.3888957Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:00:32.3889017Z         
2025-03-14T05:00:32.3889463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:32.3889625Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:00:32.3889790Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:00:32.3889933Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:00:32.3890006Z         
2025-03-14T05:00:32.3890402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:32.3890587Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:00:32.3890649Z         
2025-03-14T05:00:32.3891000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:32.3891149Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:00:32.3891222Z         
2025-03-14T05:00:32.3891632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:32.3891802Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:32.3891934Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3892101Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:00:32.3892169Z         
2025-03-14T05:00:32.3892531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:32.3892670Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:32.3892814Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:32.3892984Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:00:32.3893064Z         
2025-03-14T05:00:32.3893410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:32.3893551Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3893654Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:00:32.3893794Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:00:32.3893857Z         
2025-03-14T05:00:32.3894192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:32.3894350Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:32.3894460Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:00:32.3894605Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:00:32.3894670Z         
2025-03-14T05:00:32.3895019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:32.3895179Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:32.3895304Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:00:32.3895369Z         
2025-03-14T05:00:32.3895689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:32.3895847Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:32.3895969Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:00:32.3896033Z         
2025-03-14T05:00:32.3896353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:32.3896505Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:32.3896627Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:00:32.3896692Z         
2025-03-14T05:00:32.3897017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:32.3897223Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:32.3897345Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:00:32.3897409Z         
2025-03-14T05:00:32.3897783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:32.3897946Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:32.3898019Z         
2025-03-14T05:00:32.3898368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:32.3898518Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:32.3898580Z         
2025-03-14T05:00:32.3898952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:32.3899095Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:32.3899234Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:00:32.3899393Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:32.3899542Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:00:32.3899604Z         
2025-03-14T05:00:32.3899969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:32.3900124Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:32.3900253Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:00:32.3900415Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:32.3900575Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:00:32.3900646Z         
2025-03-14T05:00:32.3900992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:32.3901121Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:32.3901287Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:32.3901431Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:00:32.3901494Z         
2025-03-14T05:00:32.3901849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:32.3901971Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:32.3902153Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:32.3902294Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:00:32.3902364Z         
2025-03-14T05:00:32.3902688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:32.3902796Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:32.3902920Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:32.3903001Z         
2025-03-14T05:00:32.3903325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:32.3903425Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:32.3903557Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:32.3903625Z         
2025-03-14T05:00:32.3903949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:32.3904070Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:32.3904196Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:32.3904264Z         
2025-03-14T05:00:32.3904562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:32.3904680Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:32.3904805Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:32.3904874Z         
2025-03-14T05:00:32.3905222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:32.3905409Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:32.3905470Z         
2025-03-14T05:00:32.3905810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:32.3905971Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:00:32.3906040Z         
2025-03-14T05:00:32.3906428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:32.3906629Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:00:32.3906690Z         
2025-03-14T05:00:32.3907095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:32.3907304Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:00:32.3907374Z         
2025-03-14T05:00:32.3907816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:32.3907974Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:00:32.3908131Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:00:32.3908269Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:00:32.3908339Z         
2025-03-14T05:00:32.3908711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:32.3908884Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:00:32.3908943Z         
2025-03-14T05:00:32.3909271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:32.3909416Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:00:32.3909485Z         
2025-03-14T05:00:32.3909808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:32.3909991Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:00:32.3910116Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3910271Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:00:32.3910333Z         
2025-03-14T05:00:32.3910659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:32.3910784Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:00:32.3910912Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:00:32.3911065Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:00:32.3911133Z         
2025-03-14T05:00:32.3911444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:32.3911573Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3911663Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:00:32.3911803Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:00:32.3911863Z         
2025-03-14T05:00:32.3912186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:32.3912332Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:00:32.3912447Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:00:32.3912577Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:00:32.3912647Z         
2025-03-14T05:00:32.3912946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:32.3913109Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:32.3913219Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:00:32.3913285Z         
2025-03-14T05:00:32.3913579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:32.3913735Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:32.3913847Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:00:32.3913918Z         
2025-03-14T05:00:32.3914212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:32.3914366Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:32.3914473Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:00:32.3914539Z         
2025-03-14T05:00:32.3914846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:32.3915043Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:00:32.3915159Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:00:32.3915239Z         
2025-03-14T05:00:32.3915595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:32.3915736Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:00:32.3915801Z         
2025-03-14T05:00:32.3916135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:32.3916277Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:00:32.3916337Z         
2025-03-14T05:00:32.3916694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:32.3916830Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:00:32.3916965Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:00:32.3917120Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:00:32.3917266Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:00:32.3917326Z         
2025-03-14T05:00:32.3917686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:32.3917821Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:00:32.3917950Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:00:32.3918120Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:00:32.3918266Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:00:32.3918327Z         
2025-03-14T05:00:32.3918663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:32.3918776Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:00:32.3918945Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:00:32.3919076Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:00:32.3919147Z         
2025-03-14T05:00:32.3919472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:32.3919595Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:00:32.3919764Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:00:32.3919904Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:00:32.3919977Z         
2025-03-14T05:00:32.3920290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:32.3920385Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:00:32.3920507Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:00:32.3920595Z         
2025-03-14T05:00:32.3920914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:32.3921025Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:00:32.3921146Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:00:32.3921221Z         
2025-03-14T05:00:32.3921527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:32.3921641Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:00:32.3921781Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:00:32.3921842Z         
2025-03-14T05:00:32.3922180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:32.3922301Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:00:32.3922431Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:00:32.3922502Z         
2025-03-14T05:00:32.3922845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:32.3923044Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:00:32.3923104Z         
2025-03-14T05:00:32.3923436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:32.3923601Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:00:32.3923669Z         
2025-03-14T05:00:32.3924044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:32.3924244Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:00:32.3924305Z         
2025-03-14T05:00:32.3924710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:32.3924911Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:00:32.3924977Z         
2025-03-14T05:00:32.3925403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:32.3925558Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:00:32.3925705Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:00:32.3925846Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:00:32.3925905Z         
2025-03-14T05:00:32.3926276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:32.3926440Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:00:32.3926507Z         
2025-03-14T05:00:32.3926832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:32.3926983Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:00:32.3927070Z         
2025-03-14T05:00:32.3927404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:32.3927530Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:00:32.3927657Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3927802Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:00:32.3927871Z         
2025-03-14T05:00:32.3928189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:32.3928317Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:00:32.3928441Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:00:32.3928602Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:00:32.3928669Z         
2025-03-14T05:00:32.3929000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:32.3929128Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3929219Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:00:32.3929363Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:00:32.3929426Z         
2025-03-14T05:00:32.3929756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:32.3929924Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:00:32.3930027Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:00:32.3930160Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:00:32.3930231Z         
2025-03-14T05:00:32.3930545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:32.3930709Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:32.3930825Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:00:32.3930896Z         
2025-03-14T05:00:32.3931210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:32.3931435Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:32.3931566Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:00:32.3931640Z         
2025-03-14T05:00:32.3931954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:32.3932114Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:32.3932227Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:00:32.3932301Z         
2025-03-14T05:00:32.3932636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:32.3932836Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:00:32.3932968Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:00:32.3933040Z         
2025-03-14T05:00:32.3933404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:32.3933560Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:00:32.3933625Z         
2025-03-14T05:00:32.3933986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:32.3934130Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:00:32.3934201Z         
2025-03-14T05:00:32.3934567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:32.3934715Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:00:32.3934843Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:00:32.3935012Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:00:32.3935163Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:00:32.3935226Z         
2025-03-14T05:00:32.3935606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:32.3935746Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:00:32.3935881Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:00:32.3936055Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:00:32.3936206Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:00:32.3936269Z         
2025-03-14T05:00:32.3936622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:32.3936741Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:00:32.3936914Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:00:32.3937053Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:00:32.3937123Z         
2025-03-14T05:00:32.3937470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:32.3937594Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:00:32.3937763Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:00:32.3937907Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:00:32.3937970Z         
2025-03-14T05:00:32.3938301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:32.3938399Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:00:32.3938544Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:00:32.3938609Z         
2025-03-14T05:00:32.3938942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:32.3939056Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:00:32.3939202Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:00:32.3939266Z         
2025-03-14T05:00:32.3939590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:32.3939707Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:00:32.3939852Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:00:32.3939915Z         
2025-03-14T05:00:32.3940240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:32.3940357Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:00:32.3940497Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:00:32.3940559Z         
2025-03-14T05:00:32.3940932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:32.3941128Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:00:32.3941199Z         
2025-03-14T05:00:32.3941544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:32.3941710Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:00:32.3941785Z         
2025-03-14T05:00:32.3942168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:32.3942339Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:00:32.3942407Z         
2025-03-14T05:00:32.3942800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:32.3943004Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:00:32.3943063Z         
2025-03-14T05:00:32.3943498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:32.3943651Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:00:32.3943798Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:00:32.3943937Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:00:32.3944000Z         
2025-03-14T05:00:32.3944375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:32.3944541Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:00:32.3944634Z         
2025-03-14T05:00:32.3944946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:32.3945114Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:00:32.3945173Z         
2025-03-14T05:00:32.3945504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:32.3945631Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:00:32.3945758Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3945902Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:00:32.3945970Z         
2025-03-14T05:00:32.3946286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:32.3946413Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:00:32.3946530Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:00:32.3946682Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:00:32.3946743Z         
2025-03-14T05:00:32.3947058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:32.3947179Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3947272Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:00:32.3947400Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:00:32.3947470Z         
2025-03-14T05:00:32.3947778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:32.3947947Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:00:32.3948039Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:00:32.3948173Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:00:32.3948232Z         
2025-03-14T05:00:32.3948539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:32.3948688Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:32.3948806Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:00:32.3948866Z         
2025-03-14T05:00:32.3949171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:32.3949319Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:32.3949435Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:00:32.3949495Z         
2025-03-14T05:00:32.3949798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:32.3949947Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:32.3950054Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:00:32.3950121Z         
2025-03-14T05:00:32.3950447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:32.3950635Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:00:32.3950757Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:00:32.3950824Z         
2025-03-14T05:00:32.3951165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:32.3951311Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:00:32.3951371Z         
2025-03-14T05:00:32.3951711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:32.3951844Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:00:32.3951911Z         
2025-03-14T05:00:32.3952255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:32.3952397Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:00:32.3952517Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:00:32.3952673Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:00:32.3952809Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:00:32.3952877Z         
2025-03-14T05:00:32.3953228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:32.3953369Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:00:32.3953504Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:00:32.3953658Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:00:32.3953790Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:00:32.3953858Z         
2025-03-14T05:00:32.3954184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:32.3954301Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:00:32.3954459Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:00:32.3954596Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:00:32.3954656Z         
2025-03-14T05:00:32.3954989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:32.3955099Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:00:32.3955267Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:00:32.3955394Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:00:32.3955462Z         
2025-03-14T05:00:32.3955767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:32.3955884Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:00:32.3955997Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:00:32.3956063Z         
2025-03-14T05:00:32.3956365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:32.3956493Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:00:32.3956616Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:00:32.3956676Z         
2025-03-14T05:00:32.3956983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:32.3957096Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:00:32.3957233Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:00:32.3957294Z         
2025-03-14T05:00:32.3957599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:32.3957712Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:00:32.3957847Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:00:32.3957908Z         
2025-03-14T05:00:32.3958259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:32.3958445Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:00:32.3958511Z         
2025-03-14T05:00:32.3958841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:32.3959005Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:00:32.3959086Z         
2025-03-14T05:00:32.3959478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:32.3959650Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:00:32.3959718Z         
2025-03-14T05:00:32.3960117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:32.3960326Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:00:32.3960387Z         
2025-03-14T05:00:32.3960935Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:32.3961090Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:00:32.3961248Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:00:32.3961380Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:00:32.3961452Z         
2025-03-14T05:00:32.3961817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:32.3962029Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:00:32.3962090Z         
2025-03-14T05:00:32.3962412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:32.3962585Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:00:32.3962673Z         
2025-03-14T05:00:32.3962990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:32.3963130Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:00:32.3963256Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3963413Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:00:32.3963478Z         
2025-03-14T05:00:32.3963806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:32.3963932Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:00:32.3964046Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:00:32.3964198Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:00:32.3964257Z         
2025-03-14T05:00:32.3964570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:32.3964686Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:00:32.3964778Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:00:32.3964905Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:00:32.3964973Z         
2025-03-14T05:00:32.3965281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:32.3965454Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:00:32.3965541Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:00:32.3965671Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:00:32.3965732Z         
2025-03-14T05:00:32.3966037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:32.3966186Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:32.3966303Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:00:32.3966363Z         
2025-03-14T05:00:32.3966662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:32.3966811Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:32.3966928Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:00:32.3966988Z         
2025-03-14T05:00:32.3967287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:32.3967432Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:32.3967546Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:00:32.3967605Z         
2025-03-14T05:00:32.3967924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:32.3968105Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:00:32.3968234Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:00:32.3968346Z         
2025-03-14T05:00:32.3968686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:32.3968818Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:00:32.3968884Z         
2025-03-14T05:00:32.3969211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:32.3969348Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:00:32.3969409Z         
2025-03-14T05:00:32.3969759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:32.3969891Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:00:32.3970017Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:00:32.3970171Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:00:32.3970303Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:00:32.3970367Z         
2025-03-14T05:00:32.3970711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:32.3970848Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:00:32.3970995Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:00:32.3971156Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:00:32.3971294Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:00:32.3971362Z         
2025-03-14T05:00:32.3971761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:32.3971884Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:00:32.3972053Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:00:32.3972196Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:00:32.3972268Z         
2025-03-14T05:00:32.3972643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:32.3972769Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:00:32.3972957Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:00:32.3973098Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:00:32.3973176Z         
2025-03-14T05:00:32.3973518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:32.3973663Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:00:32.3973783Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:00:32.3973854Z         
2025-03-14T05:00:32.3974192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:32.3974313Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:00:32.3974431Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:00:32.3974499Z         
2025-03-14T05:00:32.3974816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:32.3974940Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:00:32.3975075Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:00:32.3975146Z         
2025-03-14T05:00:32.3975457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:32.3975583Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:00:32.3975719Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:00:32.3975789Z         
2025-03-14T05:00:32.3976147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:32.3976347Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:00:32.3976412Z         
2025-03-14T05:00:32.3976764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:32.3976927Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:00:32.3977019Z         
2025-03-14T05:00:32.3977418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:32.3977604Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:00:32.3977669Z         
2025-03-14T05:00:32.3978182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:00:32.3978329Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:00:32.3978402Z         
2025-03-14T05:00:32.3978722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3978874Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:00:32.3978947Z         
2025-03-14T05:00:32.3979410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:32.3979537Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:00:32.3979646Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:00:32.3979774Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:00:32.3979841Z         
2025-03-14T05:00:32.3980352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:32.3980508Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:32.3980765Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:00:32.3980831Z         
2025-03-14T05:00:32.3981324Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:32.3981496Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:32.3981568Z         
2025-03-14T05:00:32.3981879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3982013Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:00:32.3982077Z         
2025-03-14T05:00:32.3982554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:32.3982669Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:00:32.3982782Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:00:32.3982897Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:00:32.3982967Z         
2025-03-14T05:00:32.3983428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:32.3983587Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:32.3983818Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:00:32.3983889Z         
2025-03-14T05:00:32.3984338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:32.3984508Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:32.3984568Z         
2025-03-14T05:00:32.3984866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3984995Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:00:32.3985058Z         
2025-03-14T05:00:32.3985491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:32.3985605Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:00:32.3985714Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:00:32.3985827Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:00:32.3985894Z         
2025-03-14T05:00:32.3986360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:32.3986502Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:32.3986734Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:00:32.3986820Z         
2025-03-14T05:00:32.3987279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:32.3987446Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:32.3987507Z         
2025-03-14T05:00:32.3987801Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3987923Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:00:32.3987988Z         
2025-03-14T05:00:32.3988411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:32.3988531Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:00:32.3988632Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:00:32.3988753Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:00:32.3988815Z         
2025-03-14T05:00:32.3989279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:32.3989413Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:32.3989653Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:00:32.3989731Z         
2025-03-14T05:00:32.3990192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:32.3990349Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:32.3990417Z         
2025-03-14T05:00:32.3990707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3990837Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:00:32.3990898Z         
2025-03-14T05:00:32.3991334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:32.3991453Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:00:32.3991553Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:00:32.3991675Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:00:32.3991736Z         
2025-03-14T05:00:32.3992198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:32.3992377Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:00:32.3992616Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:00:32.3992693Z         
2025-03-14T05:00:32.3993178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:32.3993338Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:32.3993406Z         
2025-03-14T05:00:32.3993694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.3993818Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:00:32.3993878Z         
2025-03-14T05:00:32.3994162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:32.3994535Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:00:32.3994605Z         
2025-03-14T05:00:32.3994883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:32.3995348Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:00:32.3995409Z         
2025-03-14T05:00:32.3995692Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:32.3995907Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:00:32.3995975Z         
2025-03-14T05:00:32.3996368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:00:32.3996505Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:00:32.3996574Z         
2025-03-14T05:00:32.3996871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:32.3997029Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:00:32.3997093Z         
2025-03-14T05:00:32.3997495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:00:32.3997634Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:00:32.3997714Z         
2025-03-14T05:00:32.3998197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:00:32.3998339Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:00:32.3998458Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:32.3998635Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:00:32.3998763Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:00:32.3998835Z         
2025-03-14T05:00:32.3999214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:00:32.3999350Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:00:32.3999410Z         
2025-03-14T05:00:32.3999417Z 
2025-03-14T05:00:32.3999520Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:32.4066956Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:00:32.4067518Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:00:32.4067948Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4068421Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4068871Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4069319Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4069759Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4070199Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4070692Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4071154Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4071630Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4072061Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4072485Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4072959Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4073402Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4073843Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4074267Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4074687Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4075193Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4075677Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4076115Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4076557Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4077006Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:32.4077513Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4077987Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4078442Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:32.4078931Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:32.4079316Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4079764Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4080216Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4080645Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4081078Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4081471Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4081963Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4082418Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4082872Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4083300Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4083681Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4084137Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4084581Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4085012Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4085428Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4085819Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4086308Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4086760Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4087197Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4087648Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4088095Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4088613Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4089107Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4089597Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4090120Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4090573Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4091089Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4091676Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4092182Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4092659Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4093089Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4093554Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4093998Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4094443Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4094858Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4095254Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4095711Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4096168Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4096596Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4097043Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4097444Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4097945Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4098402Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4098836Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4099260Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4099671Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:32.4100146Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4100608Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4101062Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:32.4101525Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:32.4101914Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4102377Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4102822Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4103266Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4103682Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4104074Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4104556Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4105008Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4105484Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4105902Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4106300Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4106749Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4107214Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4107647Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4108062Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4108455Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4108929Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4109384Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4109817Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4110230Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4110625Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4111076Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4111544Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4111976Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4112440Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4112837Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4113293Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4113750Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4114179Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4114599Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4114983Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4115445Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4115926Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4116353Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4116801Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4117204Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4117665Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4118100Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4118522Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4118957Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4119333Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4119809Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4120246Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4120673Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4121075Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4121462Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4121910Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4122346Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4122765Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4123191Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4123578Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4124021Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4124468Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4124898Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4125304Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4125689Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4126189Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4126665Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4127084Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4127497Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4127907Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:32.4128373Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4128847Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4129290Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:32.4129736Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:32.4130152Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4130683Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4131197Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4131781Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4132276Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4132720Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4133476Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4134095Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4134538Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4135015Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4135404Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4135865Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4136312Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4136752Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4137169Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4137562Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4138024Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4138490Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4138926Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4139343Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4139748Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4140157Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4140564Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4140952Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4141339Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4141711Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4142126Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4142533Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4142920Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4143292Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4143649Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4144053Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4144468Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4144848Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4145256Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4145612Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4146020Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4146435Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4146823Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4147206Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4147577Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4147984Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4148415Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4148789Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4149159Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4149495Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4149907Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4150315Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4150710Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4151095Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4151462Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4151872Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4152271Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4152660Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4153029Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4153388Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4153794Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4154208Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4154606Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4155030Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4155406Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4155839Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4156267Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4156684Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4157078Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4157448Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4157877Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4158332Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4158746Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4159152Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4159498Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4159916Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4160319Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4160789Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4161230Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4161627Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4162044Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4162443Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4162839Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4163222Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4163571Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4163985Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4164387Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4164807Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4165186Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4165551Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4165970Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4166372Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4166769Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4167145Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4167540Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:32.4167963Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4168423Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4168842Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:32.4169266Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:32.4169652Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4170095Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4170538Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4170950Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4171418Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4171867Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4172350Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4172806Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4173211Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4173596Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4173942Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4174353Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4174794Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4175241Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4175641Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4176011Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:32.4176446Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4176870Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4177284Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:32.4177684Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:32.4178049Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:32.4178481Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4178924Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4179332Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:32.4179732Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:32.4180093Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:32.4180527Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:32.4180951Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:32.4181374Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:32.4181770Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:32.4182054Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T05:00:32.4182290Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T05:00:32.4182527Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T05:00:32.4182757Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T05:00:32.4182992Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T05:00:32.4183224Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T05:00:32.4183454Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T05:00:32.4183682Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T05:00:32.4183911Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T05:00:32.4184152Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T05:00:32.4184392Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T05:00:32.4184618Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T05:00:32.4184858Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T05:00:32.4185127Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T05:00:32.4185376Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T05:00:32.4185609Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T05:00:32.4186007Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:00:32.4186411Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:00:32.4186800Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:00:32.4187176Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:00:32.4187556Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:00:32.4187905Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:00:32.4188265Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:00:32.4188658Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:00:32.4189086Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:00:32.4189476Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:00:32.4189842Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:00:32.4189907Z         
2025-03-14T05:00:32.4190215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4190813Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4190879Z         
2025-03-14T05:00:32.4191178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4193046Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4193134Z         
2025-03-14T05:00:32.4193454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:00:32.4193603Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:00:32.4193677Z         
2025-03-14T05:00:32.4194062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:00:32.4194324Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:00:32.4194388Z         
2025-03-14T05:00:32.4194667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4195208Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4195282Z         
2025-03-14T05:00:32.4195562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4197483Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4197556Z         
2025-03-14T05:00:32.4197865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4198018Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:00:32.4198083Z         
2025-03-14T05:00:32.4198357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4198893Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4198987Z         
2025-03-14T05:00:32.4199276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4201080Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4201151Z         
2025-03-14T05:00:32.4201436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4201580Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:00:32.4201639Z         
2025-03-14T05:00:32.4201894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4202413Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4202499Z         
2025-03-14T05:00:32.4202785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4204663Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4204737Z         
2025-03-14T05:00:32.4205000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4205544Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:32.4205616Z         
2025-03-14T05:00:32.4205890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4207883Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4207960Z         
2025-03-14T05:00:32.4208272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4208438Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:00:32.4208503Z         
2025-03-14T05:00:32.4208826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4208989Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:00:32.4209064Z         
2025-03-14T05:00:32.4209360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4209932Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4210027Z         
2025-03-14T05:00:32.4210338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4212409Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4212488Z         
2025-03-14T05:00:32.4212824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4212987Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:00:32.4213067Z         
2025-03-14T05:00:32.4213353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4213956Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4214024Z         
2025-03-14T05:00:32.4214326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4216343Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4216416Z         
2025-03-14T05:00:32.4216742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4216918Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:00:32.4216994Z         
2025-03-14T05:00:32.4217272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4217876Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4217944Z         
2025-03-14T05:00:32.4218247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4220307Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4220377Z         
2025-03-14T05:00:32.4220703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4220869Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:00:32.4220961Z         
2025-03-14T05:00:32.4221261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4221410Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:00:32.4221477Z         
2025-03-14T05:00:32.4221717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4222193Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4222251Z         
2025-03-14T05:00:32.4222515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4224289Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4224375Z         
2025-03-14T05:00:32.4224677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4224818Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:00:32.4224885Z         
2025-03-14T05:00:32.4225131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4225626Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4225690Z         
2025-03-14T05:00:32.4225960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4227753Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4227837Z         
2025-03-14T05:00:32.4228127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4228261Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:00:32.4228330Z         
2025-03-14T05:00:32.4228574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4229083Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4229144Z         
2025-03-14T05:00:32.4229408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4231257Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4231333Z         
2025-03-14T05:00:32.4231614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4231764Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:00:32.4231834Z         
2025-03-14T05:00:32.4232118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4232276Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:00:32.4232339Z         
2025-03-14T05:00:32.4232594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4233081Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4233149Z         
2025-03-14T05:00:32.4233419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4235238Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4235368Z         
2025-03-14T05:00:32.4235649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4235795Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:00:32.4235865Z         
2025-03-14T05:00:32.4236116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4236616Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4236676Z         
2025-03-14T05:00:32.4236961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4238779Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4238862Z         
2025-03-14T05:00:32.4239155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4239299Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:00:32.4239368Z         
2025-03-14T05:00:32.4239617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4240122Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4240183Z         
2025-03-14T05:00:32.4240453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4242289Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4242354Z         
2025-03-14T05:00:32.4242611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4243106Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:32.4243175Z         
2025-03-14T05:00:32.4243439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4245340Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4245427Z         
2025-03-14T05:00:32.4245703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4245861Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:00:32.4245923Z         
2025-03-14T05:00:32.4246212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4246359Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:00:32.4246429Z         
2025-03-14T05:00:32.4246674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4247171Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4247255Z         
2025-03-14T05:00:32.4247517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4249345Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4249409Z         
2025-03-14T05:00:32.4249702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4249855Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:00:32.4249919Z         
2025-03-14T05:00:32.4250183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4250736Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4250822Z         
2025-03-14T05:00:32.4251122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4253120Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4253197Z         
2025-03-14T05:00:32.4253483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4253628Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:00:32.4253688Z         
2025-03-14T05:00:32.4253944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4254434Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4254526Z         
2025-03-14T05:00:32.4254791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4256698Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4256774Z         
2025-03-14T05:00:32.4257066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4257237Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:00:32.4257300Z         
2025-03-14T05:00:32.4257619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4257777Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:00:32.4257862Z         
2025-03-14T05:00:32.4258138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4258662Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4258722Z         
2025-03-14T05:00:32.4258994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4261002Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4261076Z         
2025-03-14T05:00:32.4261383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4261569Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:00:32.4261641Z         
2025-03-14T05:00:32.4261904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4262433Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4262504Z         
2025-03-14T05:00:32.4262781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4264727Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4264795Z         
2025-03-14T05:00:32.4265107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4265307Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:00:32.4265372Z         
2025-03-14T05:00:32.4265637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4266168Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4266242Z         
2025-03-14T05:00:32.4266519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4268465Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4268558Z         
2025-03-14T05:00:32.4268865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4269024Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:00:32.4269084Z         
2025-03-14T05:00:32.4269372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4269516Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:00:32.4269582Z         
2025-03-14T05:00:32.4269833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4270323Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4270385Z         
2025-03-14T05:00:32.4270655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4272500Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4272585Z         
2025-03-14T05:00:32.4272879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4273017Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:00:32.4273083Z         
2025-03-14T05:00:32.4273332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4273834Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4273895Z         
2025-03-14T05:00:32.4274162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4276020Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4276100Z         
2025-03-14T05:00:32.4276391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4276530Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:00:32.4276598Z         
2025-03-14T05:00:32.4276846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4277355Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4277424Z         
2025-03-14T05:00:32.4277685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4279515Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4279599Z         
2025-03-14T05:00:32.4279876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4280034Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:00:32.4280097Z         
2025-03-14T05:00:32.4280384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4280530Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:00:32.4280599Z         
2025-03-14T05:00:32.4280843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4281332Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4281395Z         
2025-03-14T05:00:32.4281683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4283462Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4283532Z         
2025-03-14T05:00:32.4283823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4283969Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:00:32.4284041Z         
2025-03-14T05:00:32.4284300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4284835Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4284916Z         
2025-03-14T05:00:32.4285199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4287158Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4287227Z         
2025-03-14T05:00:32.4287541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4287686Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:00:32.4287760Z         
2025-03-14T05:00:32.4288037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4288594Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4288688Z         
2025-03-14T05:00:32.4288993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4290985Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4291057Z         
2025-03-14T05:00:32.4291347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4291974Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:32.4292056Z         
2025-03-14T05:00:32.4292360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4294406Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4294484Z         
2025-03-14T05:00:32.4294781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4294940Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:00:32.4295012Z         
2025-03-14T05:00:32.4295308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4295463Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:00:32.4295527Z         
2025-03-14T05:00:32.4295795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4296315Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4296386Z         
2025-03-14T05:00:32.4296661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4298563Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4298639Z         
2025-03-14T05:00:32.4298935Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4299093Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:00:32.4299157Z         
2025-03-14T05:00:32.4299424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4299977Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4300049Z         
2025-03-14T05:00:32.4300331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4302148Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4302227Z         
2025-03-14T05:00:32.4302501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4302633Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:00:32.4302705Z         
2025-03-14T05:00:32.4302954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4303432Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4303496Z         
2025-03-14T05:00:32.4303757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4305549Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4305621Z         
2025-03-14T05:00:32.4305917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4306073Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:00:32.4306150Z         
2025-03-14T05:00:32.4306457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4306598Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:00:32.4306669Z         
2025-03-14T05:00:32.4306918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4307415Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4307488Z         
2025-03-14T05:00:32.4307768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4309589Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4309671Z         
2025-03-14T05:00:32.4309971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4310118Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:00:32.4310183Z         
2025-03-14T05:00:32.4310454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4310974Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4311051Z         
2025-03-14T05:00:32.4311315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4313164Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4313277Z         
2025-03-14T05:00:32.4313576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4313720Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:00:32.4313783Z         
2025-03-14T05:00:32.4314053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4314566Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4314642Z         
2025-03-14T05:00:32.4314922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4316786Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4316876Z         
2025-03-14T05:00:32.4317154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4317302Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:00:32.4317362Z         
2025-03-14T05:00:32.4317649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4317787Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:00:32.4317858Z         
2025-03-14T05:00:32.4318101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4318581Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4318642Z         
2025-03-14T05:00:32.4318910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4320717Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4320797Z         
2025-03-14T05:00:32.4321092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4321224Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:00:32.4321293Z         
2025-03-14T05:00:32.4321543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4322032Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4322101Z         
2025-03-14T05:00:32.4322366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4324158Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4324239Z         
2025-03-14T05:00:32.4324528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4324670Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:00:32.4324730Z         
2025-03-14T05:00:32.4324985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4325469Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4325537Z         
2025-03-14T05:00:32.4325813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4327756Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4327844Z         
2025-03-14T05:00:32.4328136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4328296Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:00:32.4328359Z         
2025-03-14T05:00:32.4328658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4328801Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:00:32.4328870Z         
2025-03-14T05:00:32.4329128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4329643Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4329725Z         
2025-03-14T05:00:32.4330011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4332059Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4332135Z         
2025-03-14T05:00:32.4332481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4332635Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:00:32.4332705Z         
2025-03-14T05:00:32.4332993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4333522Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4333624Z         
2025-03-14T05:00:32.4333918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4335829Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4335895Z         
2025-03-14T05:00:32.4336220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4336353Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:00:32.4336429Z         
2025-03-14T05:00:32.4336695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4337238Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4337302Z         
2025-03-14T05:00:32.4337596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4339503Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4339569Z         
2025-03-14T05:00:32.4339873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4340037Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:00:32.4340110Z         
2025-03-14T05:00:32.4340407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4340586Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:00:32.4340711Z         
2025-03-14T05:00:32.4340971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4341479Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4341542Z         
2025-03-14T05:00:32.4341823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4343729Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4343818Z         
2025-03-14T05:00:32.4344119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4344258Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:00:32.4344327Z         
2025-03-14T05:00:32.4344587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4345257Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4345327Z         
2025-03-14T05:00:32.4345615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4347559Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4347643Z         
2025-03-14T05:00:32.4347974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4348113Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:00:32.4348185Z         
2025-03-14T05:00:32.4348445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4348977Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4349041Z         
2025-03-14T05:00:32.4349328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4351141Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4351221Z         
2025-03-14T05:00:32.4351509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4351651Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:00:32.4351721Z         
2025-03-14T05:00:32.4352003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4352147Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:00:32.4352208Z         
2025-03-14T05:00:32.4352463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4352945Z         x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4353013Z         
2025-03-14T05:00:32.4353284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4355100Z         x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4355187Z         
2025-03-14T05:00:32.4355466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4355609Z         out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:00:32.4355670Z         
2025-03-14T05:00:32.4355927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4356419Z         x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4356480Z         
2025-03-14T05:00:32.4356748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4358605Z         x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4358693Z         
2025-03-14T05:00:32.4358986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4359120Z         out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:00:32.4359187Z         
2025-03-14T05:00:32.4359434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4359926Z         x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4359985Z         
2025-03-14T05:00:32.4360253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4362192Z         x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4362292Z         
2025-03-14T05:00:32.4362552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4363051Z         x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:32.4363123Z         
2025-03-14T05:00:32.4363387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4365251Z         x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4365345Z         
2025-03-14T05:00:32.4365620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4365768Z         x_93 += x_95;  out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93;  x_93 = x_95 = None
2025-03-14T05:00:32.4365828Z         
2025-03-14T05:00:32.4366109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4366249Z         out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:00:32.4366321Z         
2025-03-14T05:00:32.4366566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4367046Z         x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4367109Z         
2025-03-14T05:00:32.4367420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4369351Z         x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4369434Z         
2025-03-14T05:00:32.4369739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4369878Z         out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:00:32.4369949Z         
2025-03-14T05:00:32.4370204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4370727Z         x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4370800Z         
2025-03-14T05:00:32.4371087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4373148Z         x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4373233Z         
2025-03-14T05:00:32.4373612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4373757Z         out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99);  x_99 = None
2025-03-14T05:00:32.4373822Z         
2025-03-14T05:00:32.4374099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4374645Z         x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4374717Z         
2025-03-14T05:00:32.4374999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4376933Z         x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4377010Z         
2025-03-14T05:00:32.4377316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4377487Z         x_101 += out_55;  out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101;  x_101 = out_55 = None
2025-03-14T05:00:32.4377551Z         
2025-03-14T05:00:32.4377870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4378019Z         out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:00:32.4378089Z         
2025-03-14T05:00:32.4378367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4378902Z         x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:32.4378964Z         
2025-03-14T05:00:32.4379256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4381151Z         x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4381218Z         
2025-03-14T05:00:32.4381530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4381686Z         out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:00:32.4381760Z         
2025-03-14T05:00:32.4382021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4382572Z         x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:32.4382636Z         
2025-03-14T05:00:32.4382923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4384822Z         x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4384888Z         
2025-03-14T05:00:32.4385197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4385333Z         out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105);  x_105 = None
2025-03-14T05:00:32.4385416Z         
2025-03-14T05:00:32.4385665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4386162Z         x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:32.4386227Z         
2025-03-14T05:00:32.4386489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:32.4388314Z         x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:32.4388385Z         
2025-03-14T05:00:32.4388675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:32.4388834Z         x_107 += out_59;  out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107;  x_107 = out_59 = None
2025-03-14T05:00:32.4388911Z         
2025-03-14T05:00:32.4389216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:32.4389353Z         out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:00:32.4389421Z         
2025-03-14T05:00:32.4389669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4390250Z         x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T05:00:32.4390314Z         
2025-03-14T05:00:32.4390572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4391129Z         x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T05:00:32.4391198Z         
2025-03-14T05:00:32.4391606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:00:32.4391882Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest');  x_108 = None
2025-03-14T05:00:32.4391969Z         
2025-03-14T05:00:32.4392233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4392811Z         x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T05:00:32.4392878Z         
2025-03-14T05:00:32.4393238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:00:32.4393432Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features;  x_110 = top_down_features = None
2025-03-14T05:00:32.4393505Z         
2025-03-14T05:00:32.4393754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4394484Z         x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T05:00:32.4394564Z         
2025-03-14T05:00:32.4395076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:00:32.4395399Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T05:00:32.4395488Z         
2025-03-14T05:00:32.4395750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4396337Z         x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T05:00:32.4396406Z         
2025-03-14T05:00:32.4396754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:00:32.4396967Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1;  x_112 = top_down_features_1 = None
2025-03-14T05:00:32.4397032Z         
2025-03-14T05:00:32.4397287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4397860Z         x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T05:00:32.4397931Z         
2025-03-14T05:00:32.4398329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:00:32.4398674Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T05:00:32.4398737Z         
2025-03-14T05:00:32.4398995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4399568Z         x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T05:00:32.4399637Z         
2025-03-14T05:00:32.4399992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:00:32.4400203Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2;  x_114 = top_down_features_2 = None
2025-03-14T05:00:32.4400273Z         
2025-03-14T05:00:32.4400524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4401143Z         x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T05:00:32.4401205Z         
2025-03-14T05:00:32.4401592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T05:00:32.4401819Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T05:00:32.4401888Z         
2025-03-14T05:00:32.4402349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.4402511Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:00:32.4402571Z         
2025-03-14T05:00:32.4402874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4403015Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:00:32.4403085Z         
2025-03-14T05:00:32.4403519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.4403696Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:00:32.4403758Z         
2025-03-14T05:00:32.4404055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4404202Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:00:32.4404263Z         
2025-03-14T05:00:32.4404645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:32.4404825Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:00:32.4404949Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:00:32.4405068Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:00:32.4405137Z         
2025-03-14T05:00:32.4405468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:32.4405597Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:00:32.4405658Z         
2025-03-14T05:00:32.4405995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:32.4406114Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:00:32.4406184Z         
2025-03-14T05:00:32.4406582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:32.4406815Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:00:32.4406879Z         
2025-03-14T05:00:32.4407333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:32.4407466Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:00:32.4407955Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:00:32.4408089Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:00:32.4408252Z         x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:00:32.4408317Z         
2025-03-14T05:00:32.4408795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.4408951Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:00:32.4409024Z         
2025-03-14T05:00:32.4409337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4409483Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:00:32.4409542Z         
2025-03-14T05:00:32.4409986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.4410140Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:00:32.4410210Z         
2025-03-14T05:00:32.4410514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4410660Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:00:32.4410722Z         
2025-03-14T05:00:32.4411124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:32.4411333Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:00:32.4411510Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:00:32.4411653Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:00:32.4411718Z         
2025-03-14T05:00:32.4412093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:32.4412233Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:00:32.4412303Z         
2025-03-14T05:00:32.4412665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:32.4412807Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:00:32.4412882Z         
2025-03-14T05:00:32.4413306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:32.4413533Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:00:32.4413602Z         
2025-03-14T05:00:32.4414049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:32.4414191Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:00:32.4414657Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:00:32.4414814Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:00:32.4414962Z         x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:00:32.4415037Z         
2025-03-14T05:00:32.4415496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.4415653Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:00:32.4415717Z         
2025-03-14T05:00:32.4416040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4416181Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:00:32.4416255Z         
2025-03-14T05:00:32.4416700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.4416856Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:00:32.4416918Z         
2025-03-14T05:00:32.4417236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4417374Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:00:32.4417444Z         
2025-03-14T05:00:32.4417841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:32.4418072Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:00:32.4418184Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:00:32.4418309Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:00:32.4418381Z         
2025-03-14T05:00:32.4418732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:32.4418867Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:00:32.4418932Z         
2025-03-14T05:00:32.4419293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:32.4419419Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:00:32.4419493Z         
2025-03-14T05:00:32.4419913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:32.4420141Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:00:32.4420205Z         
2025-03-14T05:00:32.4420652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:32.4420795Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:00:32.4421265Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:00:32.4421407Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:00:32.4421548Z         x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T05:00:32.4421613Z         
2025-03-14T05:00:32.4422090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.4422239Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:00:32.4422311Z         
2025-03-14T05:00:32.4422628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4422772Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:00:32.4422833Z         
2025-03-14T05:00:32.4423279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.4423419Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:00:32.4423486Z         
2025-03-14T05:00:32.4423774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4423914Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:00:32.4423976Z         
2025-03-14T05:00:32.4424359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:32.4424575Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:00:32.4424673Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:00:32.4424793Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:00:32.4424854Z         
2025-03-14T05:00:32.4425179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:32.4425299Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:00:32.4425367Z         
2025-03-14T05:00:32.4425685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:32.4425810Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:00:32.4425872Z         
2025-03-14T05:00:32.4426254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:32.4426462Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:00:32.4426528Z         
2025-03-14T05:00:32.4426930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:32.4427079Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:00:32.4427492Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:00:32.4427649Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:00:32.4427766Z         x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T05:00:32.4427833Z         
2025-03-14T05:00:32.4428253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.4428402Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:00:32.4428464Z         
2025-03-14T05:00:32.4428757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4428891Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:00:32.4428958Z         
2025-03-14T05:00:32.4429378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:32.4429523Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:00:32.4429582Z         
2025-03-14T05:00:32.4429873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4430004Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:00:32.4430074Z         
2025-03-14T05:00:32.4430435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:32.4430652Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:00:32.4430754Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:00:32.4430868Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:00:32.4430935Z         
2025-03-14T05:00:32.4431255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:32.4431380Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:00:32.4431440Z         
2025-03-14T05:00:32.4431772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:32.4431892Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:00:32.4431960Z         
2025-03-14T05:00:32.4432333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:32.4432546Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:00:32.4432606Z         
2025-03-14T05:00:32.4433027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:32.4433164Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:00:32.4433582Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:00:32.4433741Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:00:32.4433867Z         x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T05:00:32.4433931Z         
2025-03-14T05:00:32.4434253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:32.4434390Z         tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32);  x_116 = None
2025-03-14T05:00:32.4434536Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32);  x_117 = None
2025-03-14T05:00:32.4434662Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32);  x_118 = None
2025-03-14T05:00:32.4434797Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32);  x_119 = None
2025-03-14T05:00:32.4434923Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32);  x_120 = None
2025-03-14T05:00:32.4434995Z         
2025-03-14T05:00:32.4435269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4435781Z         x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_115 = None
2025-03-14T05:00:32.4435843Z         
2025-03-14T05:00:32.4436128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:32.4436330Z         x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False);  x_121 = None
2025-03-14T05:00:32.4436412Z         
2025-03-14T05:00:32.4436802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:32.4437345Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:32.4437417Z         
2025-03-14T05:00:32.4437797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:32.4438350Z         x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_122 = None
2025-03-14T05:00:32.4438418Z         
2025-03-14T05:00:32.4438701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4439182Z         x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_113 = None
2025-03-14T05:00:32.4439251Z         
2025-03-14T05:00:32.4439539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:32.4439740Z         x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False);  x_123 = None
2025-03-14T05:00:32.4439825Z         
2025-03-14T05:00:32.4440225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:32.4440745Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:32.4440806Z         
2025-03-14T05:00:32.4441180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:32.4441726Z         x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_124 = None
2025-03-14T05:00:32.4441802Z         
2025-03-14T05:00:32.4442071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4442579Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_111 = None
2025-03-14T05:00:32.4442643Z         
2025-03-14T05:00:32.4442943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:32.4459867Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False);  x_125 = None
2025-03-14T05:00:32.4460088Z         
2025-03-14T05:00:32.4461047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:32.4461616Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:32.4461684Z         
2025-03-14T05:00:32.4462080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:32.4462604Z         x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_126 = None
2025-03-14T05:00:32.4462670Z         
2025-03-14T05:00:32.4462941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4463420Z         x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_109 = None
2025-03-14T05:00:32.4463620Z         
2025-03-14T05:00:32.4463905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:32.4464192Z         x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False);  x_127 = None
2025-03-14T05:00:32.4464253Z         
2025-03-14T05:00:32.4464679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:32.4465182Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:32.4465250Z         
2025-03-14T05:00:32.4465613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:32.4466143Z         x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_128 = None
2025-03-14T05:00:32.4466205Z         
2025-03-14T05:00:32.4466474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:32.4467280Z         x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:00:32.4467376Z         
2025-03-14T05:00:32.4467668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:32.4467852Z         x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False);  x_129 = None
2025-03-14T05:00:32.4467922Z         
2025-03-14T05:00:32.4468302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:32.4469201Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:00:32.4469265Z         
2025-03-14T05:00:32.4469639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:32.4470515Z         x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:00:32.4470579Z         
2025-03-14T05:00:32.4470931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:00:32.4471119Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:00:32.4471285Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:00:32.4471452Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:00:32.4471605Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:00:32.4471761Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:00:32.4471909Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:00:32.4472057Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:00:32.4472201Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:00:32.4472349Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:00:32.4472491Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:00:32.4472551Z         
2025-03-14T05:00:32.4472999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:00:32.4473178Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304);  x_131 = None
2025-03-14T05:00:32.4473378Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:00:32.4473556Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:00:32.4473744Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152);  x_132 = None
2025-03-14T05:00:32.4473927Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:00:32.4474098Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:00:32.4474254Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76);  x_133 = None
2025-03-14T05:00:32.4474421Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:00:32.4474597Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:00:32.4474740Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38);  x_134 = None
2025-03-14T05:00:32.4474911Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:00:32.4475078Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:00:32.4475226Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19);  x_135 = None
2025-03-14T05:00:32.4475385Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:00:32.4475558Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:00:32.4475620Z         
2025-03-14T05:00:32.4476068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:32.4476288Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:00:32.4476356Z         
2025-03-14T05:00:32.4476802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:32.4476966Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:00:32.4477113Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:00:32.4477263Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:00:32.4477326Z         
2025-03-14T05:00:32.4477706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:32.4477877Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:00:32.4477949Z         
2025-03-14T05:00:32.4478258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:32.4478404Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:00:32.4478466Z         
2025-03-14T05:00:32.4478787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:32.4478924Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:32.4479051Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:32.4479285Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:00:32.4479347Z         
2025-03-14T05:00:32.4479672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:32.4479795Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:32.4479924Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:32.4480074Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:00:32.4480142Z         
2025-03-14T05:00:32.4480452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:32.4480579Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:32.4480666Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:00:32.4480802Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:00:32.4480866Z         
2025-03-14T05:00:32.4481184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:32.4481331Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:32.4481425Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:00:32.4481552Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:00:32.4481620Z         
2025-03-14T05:00:32.4481979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:32.4482144Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:32.4482280Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:00:32.4482346Z         
2025-03-14T05:00:32.4482659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:32.4482818Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:32.4482929Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:00:32.4482998Z         
2025-03-14T05:00:32.4483293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:32.4483450Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:32.4483561Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:00:32.4483632Z         
2025-03-14T05:00:32.4483931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:32.4484117Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:32.4484223Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:00:32.4484291Z         
2025-03-14T05:00:32.4484624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:32.4484771Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:32.4484829Z         
2025-03-14T05:00:32.4485185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:32.4485326Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:32.4485387Z         
2025-03-14T05:00:32.4485734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:32.4485870Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:32.4486002Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:00:32.4486168Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:32.4486310Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:00:32.4486374Z         
2025-03-14T05:00:32.4486724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:32.4486860Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:32.4486988Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:00:32.4487137Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:32.4487277Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:00:32.4487338Z         
2025-03-14T05:00:32.4487687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:32.4487805Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:32.4487986Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:32.4488559Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:00:32.4488631Z         
2025-03-14T05:00:32.4488963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:32.4489083Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:32.4489255Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:32.4489403Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:00:32.4489467Z         
2025-03-14T05:00:32.4489803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:32.4489908Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:32.4490040Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:32.4490103Z         
2025-03-14T05:00:32.4490445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:32.4490544Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:32.4490672Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:32.4490736Z         
2025-03-14T05:00:32.4491071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:32.4491208Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:32.4491350Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:32.4491502Z         
2025-03-14T05:00:32.4491829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:32.4491956Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:32.4492088Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:32.4492162Z         
2025-03-14T05:00:32.4492526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:32.4492724Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:32.4492789Z         
2025-03-14T05:00:32.4493152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:32.4493313Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:00:32.4493381Z         
2025-03-14T05:00:32.4493769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:32.4493963Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:00:32.4494027Z         
2025-03-14T05:00:32.4494475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:32.4494715Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:00:32.4494788Z         
2025-03-14T05:00:32.4495259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:32.4495429Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:00:32.4495586Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:00:32.4495738Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:00:32.4495810Z         
2025-03-14T05:00:32.4496203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:32.4496391Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:00:32.4496454Z         
2025-03-14T05:00:32.4496792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:32.4496943Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:00:32.4497014Z         
2025-03-14T05:00:32.4497347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:32.4497492Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:00:32.4497623Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:00:32.4497805Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:00:32.4497871Z         
2025-03-14T05:00:32.4498210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:32.4498338Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:00:32.4498471Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:00:32.4498630Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:00:32.4498699Z         
2025-03-14T05:00:32.4499025Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:32.4499159Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:00:32.4499257Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:00:32.4499402Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:00:32.4499467Z         
2025-03-14T05:00:32.4499799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:32.4499953Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:00:32.4500056Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:00:32.4500190Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:00:32.4500261Z         
2025-03-14T05:00:32.4500595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:32.4500767Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:32.4500909Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:00:32.4500996Z         
2025-03-14T05:00:32.4501315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:32.4501479Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:32.4501594Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:00:32.4501665Z         
2025-03-14T05:00:32.4501979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:32.4502143Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:32.4502259Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:00:32.4502329Z         
2025-03-14T05:00:32.4502657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:32.4502851Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:00:32.4502972Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:00:32.4503036Z         
2025-03-14T05:00:32.4503398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:32.4503543Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:00:32.4503652Z         
2025-03-14T05:00:32.4503982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:32.4504131Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:00:32.4504191Z         
2025-03-14T05:00:32.4504540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:32.4504676Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:00:32.4504809Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:00:32.4504963Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:00:32.4505110Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:00:32.4505172Z         
2025-03-14T05:00:32.4505523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:32.4505661Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:00:32.4505789Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:00:32.4505938Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:00:32.4506081Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:00:32.4506154Z         
2025-03-14T05:00:32.4506488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:32.4506619Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:00:32.4506801Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:00:32.4506936Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:00:32.4507005Z         
2025-03-14T05:00:32.4507330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:32.4507448Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:00:32.4507614Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:00:32.4507753Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:00:32.4507815Z         
2025-03-14T05:00:32.4508132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:32.4508227Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:00:32.4508352Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:00:32.4508413Z         
2025-03-14T05:00:32.4508722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:32.4508813Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:00:32.4508937Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:00:32.4508999Z         
2025-03-14T05:00:32.4509304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:32.4509438Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:00:32.4509578Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:00:32.4509638Z         
2025-03-14T05:00:32.4509944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:32.4510056Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:00:32.4510191Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:00:32.4510251Z         
2025-03-14T05:00:32.4510604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:32.4510802Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:00:32.4510863Z         
2025-03-14T05:00:32.4511194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:32.4511357Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:00:32.4511424Z         
2025-03-14T05:00:32.4511802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:32.4511999Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:00:32.4512060Z         
2025-03-14T05:00:32.4512462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:32.4512704Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:00:32.4512775Z         
2025-03-14T05:00:32.4513215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:32.4513373Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:00:32.4513523Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:00:32.4513661Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:00:32.4513720Z         
2025-03-14T05:00:32.4514101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:32.4514269Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:00:32.4514336Z         
2025-03-14T05:00:32.4514647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:32.4514794Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:00:32.4514853Z         
2025-03-14T05:00:32.4515175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:32.4515300Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:00:32.4515446Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:00:32.4515592Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:00:32.4515661Z         
2025-03-14T05:00:32.4515975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:32.4516102Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:00:32.4516219Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:00:32.4516371Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:00:32.4516433Z         
2025-03-14T05:00:32.4516749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:32.4516871Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:00:32.4516967Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:00:32.4517100Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:00:32.4517170Z         
2025-03-14T05:00:32.4517479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:32.4517629Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:00:32.4517727Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:00:32.4517870Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:00:32.4517941Z         
2025-03-14T05:00:32.4518245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:32.4518419Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:32.4518546Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:00:32.4518616Z         
2025-03-14T05:00:32.4518919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:32.4519075Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:32.4519182Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:00:32.4519250Z         
2025-03-14T05:00:32.4519560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:32.4519712Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:32.4519819Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:00:32.4519886Z         
2025-03-14T05:00:32.4520183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:32.4520367Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:00:32.4520471Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:00:32.4520536Z         
2025-03-14T05:00:32.4520866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:32.4521003Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:00:32.4521080Z         
2025-03-14T05:00:32.4521406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:32.4521534Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:00:32.4521600Z         
2025-03-14T05:00:32.4521932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:32.4522066Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:00:32.4522184Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:00:32.4522338Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:00:32.4522473Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:00:32.4522539Z         
2025-03-14T05:00:32.4522871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:32.4523008Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:00:32.4523129Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:00:32.4523274Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:00:32.4523421Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:00:32.4523480Z         
2025-03-14T05:00:32.4523810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:32.4523935Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:00:32.4524116Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:00:32.4524247Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:00:32.4524313Z         
2025-03-14T05:00:32.4524632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:32.4524748Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:00:32.4524910Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:00:32.4525046Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:00:32.4525110Z         
2025-03-14T05:00:32.4525426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:32.4525519Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:00:32.4525637Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:00:32.4525697Z         
2025-03-14T05:00:32.4526007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:32.4526111Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:00:32.4526225Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:00:32.4526283Z         
2025-03-14T05:00:32.4526581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:32.4526709Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:00:32.4526841Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:00:32.4526899Z         
2025-03-14T05:00:32.4527204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:32.4527315Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:00:32.4527447Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:00:32.4527508Z         
2025-03-14T05:00:32.4527861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:32.4528050Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:00:32.4528117Z         
2025-03-14T05:00:32.4528446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:32.4528610Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:00:32.4528670Z         
2025-03-14T05:00:32.4529053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:32.4529237Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:00:32.4529308Z         
2025-03-14T05:00:32.4529700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:32.4529943Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:00:32.4530004Z         
2025-03-14T05:00:32.4530449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:32.4530601Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:00:32.4530747Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:00:32.4530886Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:00:32.4530950Z         
2025-03-14T05:00:32.4531334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:32.4531572Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:00:32.4531648Z         
2025-03-14T05:00:32.4531964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:32.4532116Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:00:32.4532179Z         
2025-03-14T05:00:32.4532507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:32.4532661Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:00:32.4532805Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:00:32.4532950Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:00:32.4533018Z         
2025-03-14T05:00:32.4533332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:32.4533458Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:00:32.4533575Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:00:32.4533728Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:00:32.4533787Z         
2025-03-14T05:00:32.4534096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:32.4534216Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:00:32.4534314Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:00:32.4534441Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:00:32.4534507Z         
2025-03-14T05:00:32.4534809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:32.4534957Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:00:32.4535062Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:00:32.4535196Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:00:32.4535256Z         
2025-03-14T05:00:32.4535570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:32.4535747Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:32.4535862Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:00:32.4535922Z         
2025-03-14T05:00:32.4536224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:32.4536370Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:32.4536487Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:00:32.4536547Z         
2025-03-14T05:00:32.4536846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:32.4536998Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:32.4537105Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:00:32.4537172Z         
2025-03-14T05:00:32.4537468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:32.4537653Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:00:32.4537758Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:00:32.4537824Z         
2025-03-14T05:00:32.4538156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:32.4538318Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:00:32.4538377Z         
2025-03-14T05:00:32.4538712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:32.4538845Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:00:32.4538912Z         
2025-03-14T05:00:32.4539248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:32.4539385Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:00:32.4539504Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:00:32.4539658Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:00:32.4539795Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:00:32.4539863Z         
2025-03-14T05:00:32.4540202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:32.4540340Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:00:32.4540457Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:00:32.4540626Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:00:32.4540762Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:00:32.4540832Z         
2025-03-14T05:00:32.4541156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:32.4541304Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:00:32.4541461Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:00:32.4541598Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:00:32.4541657Z         
2025-03-14T05:00:32.4541986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:32.4542095Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:00:32.4542262Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:00:32.4542392Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:00:32.4542459Z         
2025-03-14T05:00:32.4542762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:32.4542862Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:00:32.4542975Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:00:32.4543040Z         
2025-03-14T05:00:32.4543341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:32.4543438Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:00:32.4543547Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:00:32.4543635Z         
2025-03-14T05:00:32.4543941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:32.4544062Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:00:32.4544198Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:00:32.4544259Z         
2025-03-14T05:00:32.4544568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:32.4544681Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:00:32.4544818Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:00:32.4544878Z         
2025-03-14T05:00:32.4545229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:32.4545422Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:00:32.4545489Z         
2025-03-14T05:00:32.4545817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:32.4545987Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:00:32.4546046Z         
2025-03-14T05:00:32.4546443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:32.4546615Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:00:32.4546710Z         
2025-03-14T05:00:32.4547122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:32.4547333Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:00:32.4547394Z         
2025-03-14T05:00:32.4547824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:32.4547969Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:00:32.4548121Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:00:32.4548254Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:00:32.4548322Z         
2025-03-14T05:00:32.4548686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:32.4548856Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:00:32.4548916Z         
2025-03-14T05:00:32.4549227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:32.4549362Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:00:32.4549438Z         
2025-03-14T05:00:32.4549742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:32.4549891Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:00:32.4550012Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:00:32.4550163Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:00:32.4550234Z         
2025-03-14T05:00:32.4550542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:32.4550663Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:00:32.4550775Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:00:32.4550923Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:00:32.4550983Z         
2025-03-14T05:00:32.4551291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:32.4551409Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:00:32.4551502Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:00:32.4551625Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:00:32.4551691Z         
2025-03-14T05:00:32.4551990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:32.4552133Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:00:32.4552229Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:00:32.4552359Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:00:32.4552433Z         
2025-03-14T05:00:32.4552726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:32.4552890Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:32.4553005Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:00:32.4553065Z         
2025-03-14T05:00:32.4553357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:32.4553498Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:32.4553610Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:00:32.4553667Z         
2025-03-14T05:00:32.4553963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:32.4554108Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:32.4554220Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:00:32.4554279Z         
2025-03-14T05:00:32.4554577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:32.4554751Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:00:32.4554864Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:00:32.4554923Z         
2025-03-14T05:00:32.4555257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:32.4555404Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:00:32.4555469Z         
2025-03-14T05:00:32.4555786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:32.4555916Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:00:32.4555975Z         
2025-03-14T05:00:32.4556308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:32.4556435Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:00:32.4556554Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:00:32.4556708Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:00:32.4556837Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:00:32.4556901Z         
2025-03-14T05:00:32.4557230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:32.4557363Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:00:32.4557474Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:00:32.4557675Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:00:32.4557805Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:00:32.4557873Z         
2025-03-14T05:00:32.4558209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:32.4558346Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:00:32.4558501Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:00:32.4558633Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:00:32.4558694Z         
2025-03-14T05:00:32.4559021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:32.4559128Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:00:32.4559294Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:00:32.4559421Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:00:32.4559488Z         
2025-03-14T05:00:32.4559787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:32.4559887Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:00:32.4559999Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:00:32.4560065Z         
2025-03-14T05:00:32.4560361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:32.4560456Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:00:32.4560691Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:00:32.4560806Z         
2025-03-14T05:00:32.4561103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:32.4561223Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:00:32.4561351Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:00:32.4561418Z         
2025-03-14T05:00:32.4561712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:32.4561827Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:00:32.4561952Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:00:32.4562021Z         
2025-03-14T05:00:32.4562356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:32.4562550Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:00:32.4562612Z         
2025-03-14T05:00:32.4562936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:32.4563088Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:00:32.4563156Z         
2025-03-14T05:00:32.4563541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:32.4563714Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:00:32.4563797Z         
2025-03-14T05:00:32.4564295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:00:32.4564435Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:00:32.4564494Z         
2025-03-14T05:00:32.4564794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4564935Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:00:32.4565000Z         
2025-03-14T05:00:32.4565434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:32.4565555Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:00:32.4565657Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:00:32.4565777Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:00:32.4565837Z         
2025-03-14T05:00:32.4566302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:32.4566431Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:32.4566666Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:00:32.4566728Z         
2025-03-14T05:00:32.4567208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:32.4567376Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:32.4567443Z         
2025-03-14T05:00:32.4567736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4567861Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:00:32.4567922Z         
2025-03-14T05:00:32.4568355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:32.4568471Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:00:32.4568584Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:00:32.4568699Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:00:32.4568766Z         
2025-03-14T05:00:32.4569222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:32.4569357Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:32.4569609Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:00:32.4569679Z         
2025-03-14T05:00:32.4570137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:32.4570337Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:32.4570397Z         
2025-03-14T05:00:32.4570697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4570818Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:00:32.4570885Z         
2025-03-14T05:00:32.4571327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:32.4571493Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:00:32.4571614Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:00:32.4571732Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:00:32.4571799Z         
2025-03-14T05:00:32.4572267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:32.4572409Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:32.4572641Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:00:32.4572710Z         
2025-03-14T05:00:32.4573165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:32.4573353Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:32.4573415Z         
2025-03-14T05:00:32.4573717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4573836Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:00:32.4573903Z         
2025-03-14T05:00:32.4574335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:32.4574453Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:00:32.4574552Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:00:32.4574674Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:00:32.4574736Z         
2025-03-14T05:00:32.4575207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:32.4575335Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:32.4575575Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:00:32.4575635Z         
2025-03-14T05:00:32.4576113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:32.4576277Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:32.4576362Z         
2025-03-14T05:00:32.4576713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4576839Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:00:32.4576900Z         
2025-03-14T05:00:32.4577336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:32.4577451Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:00:32.4577551Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:00:32.4577670Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:00:32.4577731Z         
2025-03-14T05:00:32.4578191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:32.4578350Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:00:32.4578586Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:00:32.4578646Z         
2025-03-14T05:00:32.4579096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:32.4579251Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:32.4579338Z         
2025-03-14T05:00:32.4579626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:32.4579749Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:00:32.4579809Z         
2025-03-14T05:00:32.4580088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:32.4580461Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:00:32.4580529Z         
2025-03-14T05:00:32.4580811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:32.4581283Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:00:32.4581343Z         
2025-03-14T05:00:32.4581621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:32.4581816Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:00:32.4581883Z         
2025-03-14T05:00:32.4582276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:00:32.4582422Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:00:32.4582509Z         
2025-03-14T05:00:32.4582824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:32.4582971Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:00:32.4583030Z         
2025-03-14T05:00:32.4583402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:00:32.4583530Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:00:32.4583597Z         
2025-03-14T05:00:32.4584068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:00:32.4584206Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:00:32.4584321Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:32.4584476Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:00:32.4584599Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:00:32.4584665Z         
2025-03-14T05:00:32.4585019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:00:32.4585140Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:00:32.4585199Z         
2025-03-14T05:00:43.9173793Z 
2025-03-14T05:00:43.9174821Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:43.9177930Z     def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:00:43.9180345Z         l_features_p2_ = L_features_p2_
2025-03-14T05:00:43.9180609Z         l_features_p3_ = L_features_p3_
2025-03-14T05:00:43.9180859Z         l_features_p4_ = L_features_p4_
2025-03-14T05:00:43.9181091Z         l_features_p5_ = L_features_p5_
2025-03-14T05:00:43.9181348Z         l_features_p6_ = L_features_p6_
2025-03-14T05:00:43.9181877Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:00:43.9182533Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:00:43.9183210Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:00:43.9183912Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:00:43.9184458Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:00:43.9184971Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:00:43.9185460Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:00:43.9185983Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:00:43.9186567Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:00:43.9187111Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:00:43.9187637Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:00:43.9187983Z         
2025-03-14T05:00:43.9188557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9189217Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:00:43.9189483Z         
2025-03-14T05:00:43.9189865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9190385Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:00:43.9190641Z         
2025-03-14T05:00:43.9191170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9191809Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:00:43.9192073Z         
2025-03-14T05:00:43.9192452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9192940Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:00:43.9193199Z         
2025-03-14T05:00:43.9193662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:43.9194274Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:00:43.9194608Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:00:43.9194881Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:00:43.9195118Z         
2025-03-14T05:00:43.9195534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:43.9196067Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:00:43.9196318Z         
2025-03-14T05:00:43.9196725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:43.9197245Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:00:43.9197485Z         
2025-03-14T05:00:43.9197968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:43.9198613Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:00:43.9198933Z         
2025-03-14T05:00:43.9199429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:43.9200020Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:00:43.9200518Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:00:43.9201001Z         add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:00:43.9201291Z         x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:00:43.9201517Z         
2025-03-14T05:00:43.9202036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9202666Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:00:43.9202931Z         
2025-03-14T05:00:43.9203311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9203814Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:00:43.9204073Z         
2025-03-14T05:00:43.9204585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9205205Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:00:43.9205462Z         
2025-03-14T05:00:43.9205831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9206307Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:00:43.9206558Z         
2025-03-14T05:00:43.9207007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:43.9207617Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:00:43.9207966Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:00:43.9208232Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:00:43.9208468Z         
2025-03-14T05:00:43.9208884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:43.9209431Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:00:43.9209685Z         
2025-03-14T05:00:43.9210116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:43.9210663Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:00:43.9210916Z         
2025-03-14T05:00:43.9211564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:43.9212267Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:00:43.9212618Z         
2025-03-14T05:00:43.9213166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:43.9213842Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:00:43.9214403Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:00:43.9214916Z         add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:00:43.9215234Z         x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4);  add_1 = None
2025-03-14T05:00:43.9215481Z         
2025-03-14T05:00:43.9216024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9216693Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:00:43.9216975Z         
2025-03-14T05:00:43.9217373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9217899Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:00:43.9218168Z         
2025-03-14T05:00:43.9218710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9219364Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:00:43.9219637Z         
2025-03-14T05:00:43.9220028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9220532Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:00:43.9220798Z         
2025-03-14T05:00:43.9221272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:43.9221914Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:00:43.9222274Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:00:43.9222556Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:00:43.9222803Z         
2025-03-14T05:00:43.9223226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:43.9223777Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:00:43.9224033Z         
2025-03-14T05:00:43.9224461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:43.9225024Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:00:43.9225273Z         
2025-03-14T05:00:43.9225767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:43.9226428Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:00:43.9226747Z         
2025-03-14T05:00:43.9227235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:43.9227816Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:00:43.9228301Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:00:43.9228773Z         add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:00:43.9229065Z         x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4);  add_2 = None
2025-03-14T05:00:43.9229290Z         
2025-03-14T05:00:43.9229799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9230422Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:00:43.9230685Z         
2025-03-14T05:00:43.9231058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9231566Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:00:43.9231819Z         
2025-03-14T05:00:43.9232332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9232962Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:00:43.9233222Z         
2025-03-14T05:00:43.9233595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9234075Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:00:43.9234329Z         
2025-03-14T05:00:43.9234784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:43.9235394Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:00:43.9235739Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:00:43.9236009Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:00:43.9236235Z         
2025-03-14T05:00:43.9236642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:43.9237169Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:00:43.9237411Z         
2025-03-14T05:00:43.9237816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:43.9238333Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:00:43.9238570Z         
2025-03-14T05:00:43.9239041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:43.9239675Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:00:43.9239999Z         
2025-03-14T05:00:43.9240491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:43.9241085Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:00:43.9241574Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:00:43.9242050Z         add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:00:43.9242341Z         x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:00:43.9242569Z         
2025-03-14T05:00:43.9243082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9243702Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:00:43.9243965Z         
2025-03-14T05:00:43.9244340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9244840Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:00:43.9245094Z         
2025-03-14T05:00:43.9245607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9246228Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:00:43.9246486Z         
2025-03-14T05:00:43.9246854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9247329Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:00:43.9247577Z         
2025-03-14T05:00:43.9248042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:43.9248658Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:00:43.9249001Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:00:43.9249269Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:00:43.9249503Z         
2025-03-14T05:00:43.9249910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:43.9250457Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:00:43.9250715Z         
2025-03-14T05:00:43.9251146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:43.9251787Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:00:43.9252054Z         
2025-03-14T05:00:43.9252590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:43.9253242Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:00:43.9253579Z         
2025-03-14T05:00:43.9254100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:43.9254714Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:00:43.9255240Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:00:43.9255733Z         add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:00:43.9256032Z         x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:00:43.9256271Z         
2025-03-14T05:00:43.9256675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:43.9257171Z         tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:00:43.9257485Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32);  x_1 = None
2025-03-14T05:00:43.9257791Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32);  x_2 = None
2025-03-14T05:00:43.9258113Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32);  x_3 = None
2025-03-14T05:00:43.9258413Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32);  x_4 = None
2025-03-14T05:00:43.9258656Z         
2025-03-14T05:00:43.9259014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:43.9259795Z         x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p2_ = None
2025-03-14T05:00:43.9260350Z         
2025-03-14T05:00:43.9260958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:43.9261524Z         x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False);  x_5 = None
2025-03-14T05:00:43.9261827Z         
2025-03-14T05:00:43.9262312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:43.9263151Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:43.9263671Z         
2025-03-14T05:00:43.9264115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:43.9264985Z         x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_6 = None
2025-03-14T05:00:43.9265535Z         
2025-03-14T05:00:43.9265904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:43.9266623Z         x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p3_ = None
2025-03-14T05:00:43.9267140Z         
2025-03-14T05:00:43.9267497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:43.9268091Z         x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False);  x_7 = None
2025-03-14T05:00:43.9268393Z         
2025-03-14T05:00:43.9268855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:43.9269689Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:43.9270202Z         
2025-03-14T05:00:43.9270641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:43.9271455Z         x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_8 = None
2025-03-14T05:00:43.9271992Z         
2025-03-14T05:00:43.9272331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:43.9273035Z         x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p4_ = None
2025-03-14T05:00:43.9273539Z         
2025-03-14T05:00:43.9273890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:43.9274389Z         x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False);  x_9 = None
2025-03-14T05:00:43.9274680Z         
2025-03-14T05:00:43.9275132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:43.9275948Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:43.9276450Z         
2025-03-14T05:00:43.9276886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:43.9277678Z         x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_10 = None
2025-03-14T05:00:43.9278216Z         
2025-03-14T05:00:43.9278554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:43.9279285Z         x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p5_ = None
2025-03-14T05:00:43.9279788Z         
2025-03-14T05:00:43.9280141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:43.9280638Z         x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False);  x_11 = None
2025-03-14T05:00:43.9280926Z         
2025-03-14T05:00:43.9281383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:43.9282186Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:43.9282692Z         
2025-03-14T05:00:43.9283113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:43.9283877Z         x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_12 = None
2025-03-14T05:00:43.9284355Z         
2025-03-14T05:00:43.9284685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:43.9285549Z         x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:00:43.9286296Z         
2025-03-14T05:00:43.9286650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:43.9287145Z         x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False);  x_13 = None
2025-03-14T05:00:43.9287424Z         
2025-03-14T05:00:43.9287879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:43.9288917Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:00:43.9289661Z         
2025-03-14T05:00:43.9290095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:43.9291148Z         x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:00:43.9291927Z         
2025-03-14T05:00:43.9292380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:00:43.9293012Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:00:43.9293370Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:00:43.9293732Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:00:43.9294092Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:00:43.9294437Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:00:43.9294777Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:00:43.9295115Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:00:43.9295452Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:00:43.9295787Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:00:43.9296123Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:00:43.9296389Z         
2025-03-14T05:00:43.9296918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:00:43.9297548Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304);  x_15 = None
2025-03-14T05:00:43.9297953Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:00:43.9298447Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:00:43.9298841Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152);  x_16 = None
2025-03-14T05:00:43.9299226Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:00:43.9299623Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:00:43.9299992Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76);  x_17 = None
2025-03-14T05:00:43.9300352Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:00:43.9300728Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:00:43.9301091Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38);  x_18 = None
2025-03-14T05:00:43.9301446Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:00:43.9301818Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:00:43.9302171Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19);  x_19 = None
2025-03-14T05:00:43.9302521Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:00:43.9302909Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:00:43.9303190Z         
2025-03-14T05:00:43.9303679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:43.9304368Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:00:43.9304690Z         
2025-03-14T05:00:43.9305209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:43.9305852Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:00:43.9306206Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:00:43.9306554Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:00:43.9306813Z         
2025-03-14T05:00:43.9307274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:43.9307882Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:00:43.9308172Z         
2025-03-14T05:00:43.9308571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:43.9309084Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:00:43.9309343Z         
2025-03-14T05:00:43.9309766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:43.9310299Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:43.9310644Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9310998Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:00:43.9311286Z         
2025-03-14T05:00:43.9311709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:43.9312227Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:43.9312545Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:43.9312894Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:00:43.9313184Z         
2025-03-14T05:00:43.9313579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:43.9314092Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9314370Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:00:43.9314653Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:00:43.9314909Z         
2025-03-14T05:00:43.9315316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:43.9315837Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:43.9316129Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:00:43.9316401Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:00:43.9316671Z         
2025-03-14T05:00:43.9317116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:43.9317674Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:43.9318042Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:00:43.9318292Z         
2025-03-14T05:00:43.9318697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:43.9319234Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:43.9319590Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:00:43.9319847Z         
2025-03-14T05:00:43.9320257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:43.9320797Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:43.9321146Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:00:43.9321395Z         
2025-03-14T05:00:43.9321809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:43.9322383Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:43.9322760Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:00:43.9323014Z         
2025-03-14T05:00:43.9323466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:43.9324038Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:43.9324338Z         
2025-03-14T05:00:43.9324768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:43.9325306Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:43.9325574Z         
2025-03-14T05:00:43.9326020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:43.9326587Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:43.9326925Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:00:43.9327276Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:43.9327646Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:00:43.9327919Z         
2025-03-14T05:00:43.9328373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:43.9328939Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:43.9329275Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:00:43.9329646Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:43.9330032Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:00:43.9330336Z         
2025-03-14T05:00:43.9330805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:43.9331443Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:43.9331840Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:43.9332216Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:00:43.9332489Z         
2025-03-14T05:00:43.9332937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:43.9333442Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:43.9333780Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:43.9334137Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:00:43.9334394Z         
2025-03-14T05:00:43.9334792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:43.9335260Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:43.9335522Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:43.9335753Z         
2025-03-14T05:00:43.9336148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:43.9336603Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:43.9336863Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:43.9337097Z         
2025-03-14T05:00:43.9337485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:43.9337975Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:43.9338271Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:43.9338523Z         
2025-03-14T05:00:43.9338905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:43.9339374Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:43.9339663Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:43.9339911Z         
2025-03-14T05:00:43.9340342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:43.9340920Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:43.9341232Z         
2025-03-14T05:00:43.9341639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:43.9342175Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:00:43.9342447Z         
2025-03-14T05:00:43.9342900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:43.9343498Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:00:43.9343811Z         
2025-03-14T05:00:43.9344285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:43.9344955Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:00:43.9345301Z         
2025-03-14T05:00:43.9345816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:43.9346466Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:00:43.9346819Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:00:43.9347162Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:00:43.9347416Z         
2025-03-14T05:00:43.9347862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:43.9348564Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:00:43.9348844Z         
2025-03-14T05:00:43.9349226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:43.9349731Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:00:43.9350000Z         
2025-03-14T05:00:43.9350397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:43.9350897Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:00:43.9351234Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9351572Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:00:43.9351834Z         
2025-03-14T05:00:43.9352229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:43.9352724Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:00:43.9353027Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:00:43.9353364Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:00:43.9353629Z         
2025-03-14T05:00:43.9354011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:43.9354488Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9354757Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:00:43.9355031Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:00:43.9355274Z         
2025-03-14T05:00:43.9356737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:43.9358766Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:00:43.9359263Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:00:43.9360078Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:00:43.9360344Z         
2025-03-14T05:00:43.9360859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:43.9361448Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:43.9361822Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:00:43.9362066Z         
2025-03-14T05:00:43.9362476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:43.9363294Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:43.9363623Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:00:43.9363857Z         
2025-03-14T05:00:43.9364243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:43.9364744Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:43.9365067Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:00:43.9365298Z         
2025-03-14T05:00:43.9365683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:43.9366215Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:00:43.9366564Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:00:43.9366795Z         
2025-03-14T05:00:43.9367214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:43.9367738Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:00:43.9368036Z         
2025-03-14T05:00:43.9368456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:43.9368976Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:00:43.9369230Z         
2025-03-14T05:00:43.9369656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:43.9370198Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:00:43.9370528Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:00:43.9370875Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:00:43.9371238Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:00:43.9371561Z         
2025-03-14T05:00:43.9372020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:43.9372589Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:00:43.9372909Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:00:43.9373243Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:00:43.9373632Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:00:43.9373900Z         
2025-03-14T05:00:43.9374363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:43.9375738Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:00:43.9376113Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:00:43.9376475Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:00:43.9376732Z         
2025-03-14T05:00:43.9377152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:43.9377654Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:00:43.9377995Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:00:43.9378357Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:00:43.9378612Z         
2025-03-14T05:00:43.9379014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:43.9379478Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:00:43.9379749Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:00:43.9379988Z         
2025-03-14T05:00:43.9380378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:43.9380840Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:00:43.9381095Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:00:43.9381329Z         
2025-03-14T05:00:43.9381714Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:43.9382225Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:00:43.9382536Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:00:43.9382790Z         
2025-03-14T05:00:43.9383179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:43.9383651Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:00:43.9383954Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:00:43.9384205Z         
2025-03-14T05:00:43.9384634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:43.9385229Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:00:43.9385533Z         
2025-03-14T05:00:43.9385946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:43.9386496Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:00:43.9386777Z         
2025-03-14T05:00:43.9387238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:43.9387865Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:00:43.9388156Z         
2025-03-14T05:00:43.9388633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:43.9389335Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:00:43.9389655Z         
2025-03-14T05:00:43.9390164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:43.9390794Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:00:43.9391150Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:00:43.9391501Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:00:43.9391762Z         
2025-03-14T05:00:43.9392213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:43.9392803Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:00:43.9393091Z         
2025-03-14T05:00:43.9393483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:43.9393990Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:00:43.9394256Z         
2025-03-14T05:00:43.9394652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:43.9395170Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:00:43.9395480Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9395808Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:00:43.9396075Z         
2025-03-14T05:00:43.9396469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:43.9396955Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:00:43.9397256Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:00:43.9397583Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:00:43.9397847Z         
2025-03-14T05:00:43.9398247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:43.9398730Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9398998Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:00:43.9399274Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:00:43.9399520Z         
2025-03-14T05:00:43.9399907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:43.9400415Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:00:43.9400725Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:00:43.9400994Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:00:43.9401245Z         
2025-03-14T05:00:43.9401636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:43.9402178Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:43.9402509Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:00:43.9402745Z         
2025-03-14T05:00:43.9403131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:43.9403641Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:43.9403966Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:00:43.9404201Z         
2025-03-14T05:00:43.9404587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:43.9405109Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:43.9405441Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:00:43.9405677Z         
2025-03-14T05:00:43.9406078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:43.9406637Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:00:43.9406995Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:00:43.9407235Z         
2025-03-14T05:00:43.9407678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:43.9408241Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:00:43.9408505Z         
2025-03-14T05:00:43.9408933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:43.9409478Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:00:43.9409745Z         
2025-03-14T05:00:43.9410192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:43.9410755Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:00:43.9411088Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:00:43.9411523Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:00:43.9411906Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:00:43.9412182Z         
2025-03-14T05:00:43.9412638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:43.9413216Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:00:43.9413547Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:00:43.9413914Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:00:43.9414271Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:00:43.9414538Z         
2025-03-14T05:00:43.9414959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:43.9415506Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:00:43.9415846Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:00:43.9416208Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:00:43.9416471Z         
2025-03-14T05:00:43.9416898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:43.9417411Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:00:43.9417751Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:00:43.9418120Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:00:43.9418375Z         
2025-03-14T05:00:43.9418777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:43.9419258Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:00:43.9419533Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:00:43.9419775Z         
2025-03-14T05:00:43.9420165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:43.9420636Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:00:43.9420903Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:00:43.9421161Z         
2025-03-14T05:00:43.9421559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:43.9422052Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:00:43.9422363Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:00:43.9422620Z         
2025-03-14T05:00:43.9423014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:43.9423497Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:00:43.9423803Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:00:43.9424058Z         
2025-03-14T05:00:43.9424498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:43.9425106Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:00:43.9425423Z         
2025-03-14T05:00:43.9425836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:43.9426383Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:00:43.9426665Z         
2025-03-14T05:00:43.9427143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:43.9427750Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:00:43.9428075Z         
2025-03-14T05:00:43.9428571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:43.9429228Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:00:43.9429546Z         
2025-03-14T05:00:43.9430060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:43.9430696Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:00:43.9431051Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:00:43.9431395Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:00:43.9431653Z         
2025-03-14T05:00:43.9432106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:43.9432691Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:00:43.9432978Z         
2025-03-14T05:00:43.9433372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:43.9433880Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:00:43.9434144Z         
2025-03-14T05:00:43.9434538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:43.9435052Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:00:43.9435358Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9435684Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:00:43.9435952Z         
2025-03-14T05:00:43.9436353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:43.9436846Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:00:43.9437141Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:00:43.9437468Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:00:43.9437731Z         
2025-03-14T05:00:43.9438123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:43.9438606Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9438875Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:00:43.9439147Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:00:43.9439398Z         
2025-03-14T05:00:43.9439785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:43.9440292Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:00:43.9440598Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:00:43.9440874Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:00:43.9441145Z         
2025-03-14T05:00:43.9441539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:43.9442070Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:43.9442400Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:00:43.9442642Z         
2025-03-14T05:00:43.9443038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:43.9443540Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:43.9443864Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:00:43.9444100Z         
2025-03-14T05:00:43.9444486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:43.9444986Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:43.9445305Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:00:43.9445538Z         
2025-03-14T05:00:43.9445920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:43.9446459Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:00:43.9446804Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:00:43.9447038Z         
2025-03-14T05:00:43.9447452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:43.9448005Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:00:43.9448267Z         
2025-03-14T05:00:43.9448694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:43.9449225Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:00:43.9449485Z         
2025-03-14T05:00:43.9449919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:43.9450461Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:00:43.9450780Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:00:43.9451125Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:00:43.9451575Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:00:43.9451873Z         
2025-03-14T05:00:43.9452362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:43.9452939Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:00:43.9453263Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:00:43.9453630Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:00:43.9453988Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:00:43.9454274Z         
2025-03-14T05:00:43.9454802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:43.9455330Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:00:43.9455667Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:00:43.9456028Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:00:43.9456289Z         
2025-03-14T05:00:43.9456717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:43.9457231Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:00:43.9457567Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:00:43.9457927Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:00:43.9458186Z         
2025-03-14T05:00:43.9458590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:43.9459067Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:00:43.9459338Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:00:43.9459582Z         
2025-03-14T05:00:43.9459977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:43.9460449Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:00:43.9460898Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:00:43.9461198Z         
2025-03-14T05:00:43.9461603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:43.9462095Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:00:43.9462406Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:00:43.9462672Z         
2025-03-14T05:00:43.9463058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:43.9463535Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:00:43.9463843Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:00:43.9464091Z         
2025-03-14T05:00:43.9464516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:43.9465109Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:00:43.9465403Z         
2025-03-14T05:00:43.9465811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:43.9466355Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:00:43.9466630Z         
2025-03-14T05:00:43.9467113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:43.9467718Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:00:43.9468034Z         
2025-03-14T05:00:43.9468546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:43.9469212Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:00:43.9469531Z         
2025-03-14T05:00:43.9470048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:43.9470685Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:00:43.9471037Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:00:43.9471376Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:00:43.9471627Z         
2025-03-14T05:00:43.9472083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:43.9472670Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:00:43.9472949Z         
2025-03-14T05:00:43.9473344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:43.9473866Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:00:43.9474128Z         
2025-03-14T05:00:43.9474541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:43.9475056Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:00:43.9475365Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9475688Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:00:43.9475953Z         
2025-03-14T05:00:43.9476350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:43.9476840Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:00:43.9477138Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:00:43.9477458Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:00:43.9477729Z         
2025-03-14T05:00:43.9478125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:43.9478606Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9478874Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:00:43.9479142Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:00:43.9479380Z         
2025-03-14T05:00:43.9479771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:43.9480313Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:00:43.9480610Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:00:43.9480880Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:00:43.9481142Z         
2025-03-14T05:00:43.9481542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:43.9482041Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:43.9482357Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:00:43.9482586Z         
2025-03-14T05:00:43.9482966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:43.9483460Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:43.9483772Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:00:43.9483999Z         
2025-03-14T05:00:43.9484378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:43.9484877Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:43.9485195Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:00:43.9485436Z         
2025-03-14T05:00:43.9485816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:43.9486340Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:00:43.9486677Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:00:43.9486899Z         
2025-03-14T05:00:43.9487313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:43.9487852Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:00:43.9488104Z         
2025-03-14T05:00:43.9488513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:43.9489026Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:00:43.9489275Z         
2025-03-14T05:00:43.9489699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:43.9490223Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:00:43.9490533Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:00:43.9490859Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:00:43.9491206Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:00:43.9491545Z         
2025-03-14T05:00:43.9492046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:43.9492724Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:00:43.9493043Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:00:43.9493422Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:00:43.9493786Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:00:43.9494221Z         
2025-03-14T05:00:43.9494687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:43.9495221Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:00:43.9495571Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:00:43.9495939Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:00:43.9496208Z         
2025-03-14T05:00:43.9496656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:43.9497187Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:00:43.9497534Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:00:43.9497899Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:00:43.9498159Z         
2025-03-14T05:00:43.9498574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:43.9499064Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:00:43.9499330Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:00:43.9499576Z         
2025-03-14T05:00:43.9499987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:43.9500473Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:00:43.9500770Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:00:43.9501024Z         
2025-03-14T05:00:43.9501441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:43.9501952Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:00:43.9502257Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:00:43.9502509Z         
2025-03-14T05:00:43.9502897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:43.9503375Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:00:43.9503671Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:00:43.9503923Z         
2025-03-14T05:00:43.9504358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:43.9504949Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:00:43.9505247Z         
2025-03-14T05:00:43.9505666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:43.9506215Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:00:43.9506491Z         
2025-03-14T05:00:43.9506991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:43.9507608Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:00:43.9507917Z         
2025-03-14T05:00:43.9508522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:00:43.9509234Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:00:43.9509484Z         
2025-03-14T05:00:43.9509864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9510354Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:00:43.9510610Z         
2025-03-14T05:00:43.9511125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:43.9511727Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:00:43.9511994Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:00:43.9512259Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:00:43.9512483Z         
2025-03-14T05:00:43.9513023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:43.9513662Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:43.9514075Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:00:43.9514436Z         
2025-03-14T05:00:43.9514991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:43.9515676Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:43.9515964Z         
2025-03-14T05:00:43.9516349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9516829Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:00:43.9517071Z         
2025-03-14T05:00:43.9517603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:43.9518200Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:00:43.9518475Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:00:43.9518754Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:00:43.9518988Z         
2025-03-14T05:00:43.9519525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:43.9520160Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:43.9520596Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:00:43.9520945Z         
2025-03-14T05:00:43.9521492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:43.9522197Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:43.9522482Z         
2025-03-14T05:00:43.9522863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9523337Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:00:43.9523571Z         
2025-03-14T05:00:43.9524084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:43.9524682Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:00:43.9524963Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:00:43.9525243Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:00:43.9525476Z         
2025-03-14T05:00:43.9526017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:43.9526660Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:43.9527087Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:00:43.9527442Z         
2025-03-14T05:00:43.9527976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:43.9528697Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:43.9528990Z         
2025-03-14T05:00:43.9529374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9529853Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:00:43.9530097Z         
2025-03-14T05:00:43.9530619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:43.9531227Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:00:43.9531933Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:00:43.9532232Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:00:43.9532478Z         
2025-03-14T05:00:43.9533060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:43.9533728Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:43.9534164Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:00:43.9534522Z         
2025-03-14T05:00:43.9535098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:43.9535811Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:43.9536096Z         
2025-03-14T05:00:43.9536501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9536987Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:00:43.9537233Z         
2025-03-14T05:00:43.9537759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:43.9538366Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:00:43.9538643Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:00:43.9538922Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:00:43.9539161Z         
2025-03-14T05:00:43.9539712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:43.9540395Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:00:43.9540855Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:00:43.9541205Z         
2025-03-14T05:00:43.9541758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:43.9542466Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:43.9542750Z         
2025-03-14T05:00:43.9543132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9543615Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:00:43.9543860Z         
2025-03-14T05:00:43.9544230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:43.9544954Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:00:43.9545449Z         
2025-03-14T05:00:43.9545815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:43.9546622Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:00:43.9547206Z         
2025-03-14T05:00:43.9547560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:43.9548085Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:00:43.9548416Z         
2025-03-14T05:00:43.9548887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:00:43.9549483Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:00:43.9549743Z         
2025-03-14T05:00:43.9550139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:43.9550629Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:00:43.9550892Z         
2025-03-14T05:00:43.9551347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:00:43.9551903Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:00:43.9552148Z         
2025-03-14T05:00:43.9552706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:00:43.9553374Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:00:43.9553679Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:43.9553999Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:00:43.9554331Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:00:43.9554576Z         
2025-03-14T05:00:43.9555023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:00:43.9555556Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:00:43.9555808Z         
2025-03-14T05:00:43.9556054Z 
2025-03-14T05:00:43.9556148Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:43.9558353Z     def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:00:43.9560843Z         l_features_p2_ = L_features_p2_
2025-03-14T05:00:43.9561069Z         l_features_p3_ = L_features_p3_
2025-03-14T05:00:43.9561285Z         l_features_p4_ = L_features_p4_
2025-03-14T05:00:43.9561496Z         l_features_p5_ = L_features_p5_
2025-03-14T05:00:43.9561704Z         l_features_p6_ = L_features_p6_
2025-03-14T05:00:43.9562149Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:00:43.9562702Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:00:43.9563310Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:00:43.9563840Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:00:43.9564376Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:00:43.9564885Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:00:43.9565358Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:00:43.9565882Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:00:43.9566452Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:00:43.9567003Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:00:43.9567540Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:00:43.9567891Z         
2025-03-14T05:00:43.9568427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9569066Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:00:43.9569328Z         
2025-03-14T05:00:43.9569736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9570227Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:00:43.9570476Z         
2025-03-14T05:00:43.9570986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9571786Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:00:43.9572072Z         
2025-03-14T05:00:43.9572475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9572952Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:00:43.9573211Z         
2025-03-14T05:00:43.9573664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:43.9574259Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:00:43.9574584Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:00:43.9574850Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:00:43.9575082Z         
2025-03-14T05:00:43.9575498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:43.9576012Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:00:43.9576246Z         
2025-03-14T05:00:43.9576639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:43.9577145Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:00:43.9577405Z         
2025-03-14T05:00:43.9577862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:43.9578500Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:00:43.9578815Z         
2025-03-14T05:00:43.9579308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:43.9579897Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:00:43.9580397Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:00:43.9580896Z         add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:00:43.9581181Z         x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:00:43.9581400Z         
2025-03-14T05:00:43.9581906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9582539Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:00:43.9582805Z         
2025-03-14T05:00:43.9583180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9583678Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:00:43.9583933Z         
2025-03-14T05:00:43.9584434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9585050Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:00:43.9585309Z         
2025-03-14T05:00:43.9585669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9586150Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:00:43.9586396Z         
2025-03-14T05:00:43.9586845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:43.9587452Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:00:43.9587793Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:00:43.9588064Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:00:43.9588304Z         
2025-03-14T05:00:43.9588712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:43.9589265Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:00:43.9589508Z         
2025-03-14T05:00:43.9589911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:43.9590429Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:00:43.9590686Z         
2025-03-14T05:00:43.9591154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:43.9591803Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:00:43.9592130Z         
2025-03-14T05:00:43.9592629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:43.9593220Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:00:43.9593720Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:00:43.9594205Z         add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:00:43.9594504Z         x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4);  add_1 = None
2025-03-14T05:00:43.9594735Z         
2025-03-14T05:00:43.9595255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9595892Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:00:43.9596156Z         
2025-03-14T05:00:43.9596548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9597030Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:00:43.9597288Z         
2025-03-14T05:00:43.9597813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9598446Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:00:43.9598717Z         
2025-03-14T05:00:43.9599118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9599613Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:00:43.9599871Z         
2025-03-14T05:00:43.9600355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:43.9600997Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:00:43.9601356Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:00:43.9601636Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:00:43.9601882Z         
2025-03-14T05:00:43.9602318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:43.9602879Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:00:43.9603138Z         
2025-03-14T05:00:43.9603565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:43.9604116Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:00:43.9604386Z         
2025-03-14T05:00:43.9604883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:43.9605575Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:00:43.9605921Z         
2025-03-14T05:00:43.9606448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:43.9607084Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:00:43.9607607Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:00:43.9608107Z         add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:00:43.9608415Z         x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4);  add_2 = None
2025-03-14T05:00:43.9608657Z         
2025-03-14T05:00:43.9609213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9609878Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:00:43.9610157Z         
2025-03-14T05:00:43.9610588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9611096Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:00:43.9611426Z         
2025-03-14T05:00:43.9613481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9614243Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:00:43.9614523Z         
2025-03-14T05:00:43.9614934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9615436Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:00:43.9615698Z         
2025-03-14T05:00:43.9616099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:43.9616305Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:00:43.9616409Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:00:43.9616541Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:00:43.9616606Z         
2025-03-14T05:00:43.9616954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:43.9617139Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:00:43.9617212Z         
2025-03-14T05:00:43.9617572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:43.9617732Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:00:43.9617818Z         
2025-03-14T05:00:43.9618227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:43.9618455Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:00:43.9618525Z         
2025-03-14T05:00:43.9618951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:43.9619088Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:00:43.9619401Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:00:43.9619533Z         add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:00:43.9619648Z         x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:00:43.9619718Z         
2025-03-14T05:00:43.9620160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9620311Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:00:43.9620372Z         
2025-03-14T05:00:43.9620703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9620844Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:00:43.9620913Z         
2025-03-14T05:00:43.9621353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:43.9621502Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:00:43.9621564Z         
2025-03-14T05:00:43.9621866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9622000Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:00:43.9622071Z         
2025-03-14T05:00:43.9622450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:43.9622646Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:00:43.9622753Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:00:43.9622872Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:00:43.9622940Z         
2025-03-14T05:00:43.9623273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:43.9623423Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:00:43.9623485Z         
2025-03-14T05:00:43.9623823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:43.9623984Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:00:43.9624076Z         
2025-03-14T05:00:43.9624466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:43.9624685Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:00:43.9624748Z         
2025-03-14T05:00:43.9625174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:43.9625298Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:00:43.9625624Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:00:43.9625745Z         add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:00:43.9625864Z         x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:00:43.9625926Z         
2025-03-14T05:00:43.9626241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:43.9626363Z         tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:00:43.9626496Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32);  x_1 = None
2025-03-14T05:00:43.9626617Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32);  x_2 = None
2025-03-14T05:00:43.9626763Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32);  x_3 = None
2025-03-14T05:00:43.9626879Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32);  x_4 = None
2025-03-14T05:00:43.9626949Z         
2025-03-14T05:00:43.9627213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:43.9627659Z         x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p2_ = None
2025-03-14T05:00:43.9627721Z         
2025-03-14T05:00:43.9628010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:43.9628203Z         x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False);  x_5 = None
2025-03-14T05:00:43.9628277Z         
2025-03-14T05:00:43.9628665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:43.9629088Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:43.9629156Z         
2025-03-14T05:00:43.9629545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:43.9629971Z         x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_6 = None
2025-03-14T05:00:43.9630055Z         
2025-03-14T05:00:43.9630351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:43.9630763Z         x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p3_ = None
2025-03-14T05:00:43.9630833Z         
2025-03-14T05:00:43.9631111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:43.9631305Z         x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False);  x_7 = None
2025-03-14T05:00:43.9631369Z         
2025-03-14T05:00:43.9631757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:43.9632168Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:43.9632238Z         
2025-03-14T05:00:43.9632599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:43.9633016Z         x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_8 = None
2025-03-14T05:00:43.9633105Z         
2025-03-14T05:00:43.9633362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:43.9633770Z         x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p4_ = None
2025-03-14T05:00:43.9633832Z         
2025-03-14T05:00:43.9634109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:43.9634290Z         x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False);  x_9 = None
2025-03-14T05:00:43.9634361Z         
2025-03-14T05:00:43.9634739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:43.9635148Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:43.9635212Z         
2025-03-14T05:00:43.9635587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:43.9636026Z         x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_10 = None
2025-03-14T05:00:43.9636098Z         
2025-03-14T05:00:43.9636357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:43.9636816Z         x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p5_ = None
2025-03-14T05:00:43.9636877Z         
2025-03-14T05:00:43.9637156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:43.9637338Z         x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False);  x_11 = None
2025-03-14T05:00:43.9637400Z         
2025-03-14T05:00:43.9637785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:43.9638180Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:00:43.9638252Z         
2025-03-14T05:00:43.9638611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:43.9638996Z         x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_12 = None
2025-03-14T05:00:43.9639056Z         
2025-03-14T05:00:43.9639318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:43.9639894Z         x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:00:43.9639984Z         
2025-03-14T05:00:43.9640254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:43.9640434Z         x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False);  x_13 = None
2025-03-14T05:00:43.9640494Z         
2025-03-14T05:00:43.9640870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:43.9641511Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:00:43.9641574Z         
2025-03-14T05:00:43.9641929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:43.9642538Z         x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:00:43.9642612Z         
2025-03-14T05:00:43.9642966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:00:43.9643162Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:00:43.9643303Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:00:43.9643473Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:00:43.9643614Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:00:43.9643772Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:00:43.9643911Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:00:43.9644068Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:00:43.9644201Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:00:43.9644352Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:00:43.9644481Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:00:43.9644550Z         
2025-03-14T05:00:43.9644974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:00:43.9645156Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304);  x_15 = None
2025-03-14T05:00:43.9645348Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:00:43.9645548Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:00:43.9645717Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152);  x_16 = None
2025-03-14T05:00:43.9645892Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:00:43.9646073Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:00:43.9646218Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76);  x_17 = None
2025-03-14T05:00:43.9646389Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:00:43.9646556Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:00:43.9646703Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38);  x_18 = None
2025-03-14T05:00:43.9646863Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:00:43.9647038Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:00:43.9647177Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19);  x_19 = None
2025-03-14T05:00:43.9647343Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:00:43.9647532Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:00:43.9647609Z         
2025-03-14T05:00:43.9648033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:43.9648306Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:00:43.9648371Z         
2025-03-14T05:00:43.9648829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:43.9648992Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:00:43.9649144Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:00:43.9649292Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:00:43.9649355Z         
2025-03-14T05:00:43.9649752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:43.9649930Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:00:43.9649998Z         
2025-03-14T05:00:43.9650322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:43.9650473Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:00:43.9650536Z         
2025-03-14T05:00:43.9650871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:43.9651002Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:43.9651158Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9651321Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:00:43.9651531Z         
2025-03-14T05:00:43.9651898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:43.9652043Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:43.9652177Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:43.9652354Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:00:43.9652427Z         
2025-03-14T05:00:43.9652785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:43.9652912Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9653008Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:00:43.9653141Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:00:43.9653212Z         
2025-03-14T05:00:43.9653526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:43.9653684Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:43.9653773Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:00:43.9653941Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:00:43.9654004Z         
2025-03-14T05:00:43.9654332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:43.9654513Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:43.9654662Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:00:43.9654724Z         
2025-03-14T05:00:43.9655040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:43.9655196Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:43.9655318Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:00:43.9655381Z         
2025-03-14T05:00:43.9655697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:43.9655852Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:43.9655973Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:00:43.9656035Z         
2025-03-14T05:00:43.9656349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:43.9656542Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:43.9656653Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:00:43.9656721Z         
2025-03-14T05:00:43.9657063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:43.9657213Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:43.9657294Z         
2025-03-14T05:00:43.9657645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:43.9657783Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:43.9657851Z         
2025-03-14T05:00:43.9658204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:43.9658351Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:43.9658483Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:00:43.9658649Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:43.9658791Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:00:43.9658865Z         
2025-03-14T05:00:43.9659220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:43.9659369Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:43.9659494Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:00:43.9659654Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:43.9659936Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:00:43.9660010Z         
2025-03-14T05:00:43.9660358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:43.9660506Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:43.9660859Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:43.9661011Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:00:43.9661076Z         
2025-03-14T05:00:43.9661427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:43.9661546Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:43.9661724Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:43.9661862Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:00:43.9661939Z         
2025-03-14T05:00:43.9662258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:43.9662366Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:43.9662486Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:43.9662557Z         
2025-03-14T05:00:43.9662873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:43.9662978Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:43.9663095Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:43.9663165Z         
2025-03-14T05:00:43.9663475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:43.9663624Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:43.9663754Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:43.9663823Z         
2025-03-14T05:00:43.9664128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:43.9664250Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:43.9664376Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:43.9664445Z         
2025-03-14T05:00:43.9664798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:43.9664984Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:43.9665055Z         
2025-03-14T05:00:43.9665389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:43.9665557Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:00:43.9665617Z         
2025-03-14T05:00:43.9666007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:43.9666206Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:00:43.9666275Z         
2025-03-14T05:00:43.9666677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:43.9666958Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:00:43.9667020Z         
2025-03-14T05:00:43.9667468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:43.9667624Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:00:43.9667785Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:00:43.9667924Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:00:43.9667993Z         
2025-03-14T05:00:43.9668376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:43.9668556Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:00:43.9668615Z         
2025-03-14T05:00:43.9668938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:43.9669081Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:00:43.9669152Z         
2025-03-14T05:00:43.9669470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:43.9669607Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:00:43.9669762Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9669921Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:00:43.9669983Z         
2025-03-14T05:00:43.9670307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:43.9670431Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:00:43.9670560Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:00:43.9670711Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:00:43.9670780Z         
2025-03-14T05:00:43.9671087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:43.9671219Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9671308Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:00:43.9671447Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:00:43.9671507Z         
2025-03-14T05:00:43.9671822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:43.9671969Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:00:43.9672066Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:00:43.9672209Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:00:43.9672277Z         
2025-03-14T05:00:43.9672584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:43.9672754Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:43.9672890Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:00:43.9672951Z         
2025-03-14T05:00:43.9673252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:43.9673400Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:43.9673519Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:00:43.9673580Z         
2025-03-14T05:00:43.9673880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:43.9674031Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:43.9674146Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:00:43.9674207Z         
2025-03-14T05:00:43.9674510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:43.9674694Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:00:43.9674811Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:00:43.9674871Z         
2025-03-14T05:00:43.9675211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:43.9675370Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:00:43.9675439Z         
2025-03-14T05:00:43.9675769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:43.9675914Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:00:43.9675975Z         
2025-03-14T05:00:43.9676323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:43.9676460Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:00:43.9676594Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:00:43.9676747Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:00:43.9676900Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:00:43.9676961Z         
2025-03-14T05:00:43.9677313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:43.9677449Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:00:43.9677578Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:00:43.9677734Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:00:43.9677889Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:00:43.9677961Z         
2025-03-14T05:00:43.9678294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:43.9678476Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:00:43.9678635Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:00:43.9678773Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:00:43.9678831Z         
2025-03-14T05:00:43.9679162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:43.9679271Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:00:43.9679440Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:00:43.9679568Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:00:43.9679637Z         
2025-03-14T05:00:43.9679939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:43.9680037Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:00:43.9680150Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:00:43.9680215Z         
2025-03-14T05:00:43.9680514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:43.9680611Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:00:43.9680721Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:00:43.9680787Z         
2025-03-14T05:00:43.9681099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:43.9681222Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:00:43.9681351Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:00:43.9681416Z         
2025-03-14T05:00:43.9681704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:43.9681821Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:00:43.9681949Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:00:43.9682015Z         
2025-03-14T05:00:43.9682346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:43.9682544Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:00:43.9682603Z         
2025-03-14T05:00:43.9682929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:43.9683087Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:00:43.9683155Z         
2025-03-14T05:00:43.9683541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:43.9683720Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:00:43.9683799Z         
2025-03-14T05:00:43.9684207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:43.9684406Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:00:43.9684476Z         
2025-03-14T05:00:43.9684895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:43.9685048Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:00:43.9685199Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:00:43.9685331Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:00:43.9685395Z         
2025-03-14T05:00:43.9685754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:43.9685924Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:00:43.9685984Z         
2025-03-14T05:00:43.9686298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:43.9686437Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:00:43.9686505Z         
2025-03-14T05:00:43.9686811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:43.9686964Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:00:43.9687089Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9687243Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:00:43.9687304Z         
2025-03-14T05:00:43.9687626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:43.9687748Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:00:43.9687873Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:00:43.9688024Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:00:43.9688093Z         
2025-03-14T05:00:43.9688401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:43.9688528Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9688617Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:00:43.9688753Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:00:43.9688816Z         
2025-03-14T05:00:43.9689134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:43.9689280Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:00:43.9689397Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:00:43.9689527Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:00:43.9689613Z         
2025-03-14T05:00:43.9689917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:43.9690103Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:43.9690216Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:00:43.9690289Z         
2025-03-14T05:00:43.9690590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:43.9690744Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:43.9690852Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:00:43.9690919Z         
2025-03-14T05:00:43.9691219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:43.9691438Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:43.9691564Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:00:43.9691625Z         
2025-03-14T05:00:43.9691933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:43.9692122Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:00:43.9692244Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:00:43.9692309Z         
2025-03-14T05:00:43.9692672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:43.9692843Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:00:43.9692914Z         
2025-03-14T05:00:43.9693242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:43.9693386Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:00:43.9693447Z         
2025-03-14T05:00:43.9693792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:43.9693924Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:00:43.9694053Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:00:43.9694207Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:00:43.9694350Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:00:43.9694410Z         
2025-03-14T05:00:43.9694759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:43.9694893Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:00:43.9695019Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:00:43.9695186Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:00:43.9695329Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:00:43.9695408Z         
2025-03-14T05:00:43.9695758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:43.9695874Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:00:43.9696041Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:00:43.9696173Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:00:43.9696241Z         
2025-03-14T05:00:43.9696567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:43.9696686Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:00:43.9696850Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:00:43.9696988Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:00:43.9697047Z         
2025-03-14T05:00:43.9697365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:43.9697458Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:00:43.9697578Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:00:43.9697637Z         
2025-03-14T05:00:43.9697951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:43.9698042Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:00:43.9698160Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:00:43.9698252Z         
2025-03-14T05:00:43.9698561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:43.9698674Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:00:43.9698811Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:00:43.9698870Z         
2025-03-14T05:00:43.9699181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:43.9699301Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:00:43.9699431Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:00:43.9699499Z         
2025-03-14T05:00:43.9699842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:43.9700039Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:00:43.9700101Z         
2025-03-14T05:00:43.9700437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:43.9700594Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:00:43.9700659Z         
2025-03-14T05:00:43.9701049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:43.9701229Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:00:43.9701308Z         
2025-03-14T05:00:43.9701728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:43.9701932Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:00:43.9702001Z         
2025-03-14T05:00:43.9702437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:43.9702591Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:00:43.9702739Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:00:43.9702883Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:00:43.9702944Z         
2025-03-14T05:00:43.9703326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:43.9703491Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:00:43.9703562Z         
2025-03-14T05:00:43.9703869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:43.9704018Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:00:43.9704078Z         
2025-03-14T05:00:43.9704391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:43.9704538Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:00:43.9704668Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9704814Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:00:43.9704884Z         
2025-03-14T05:00:43.9705195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:43.9705320Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:00:43.9705440Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:00:43.9705593Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:00:43.9705656Z         
2025-03-14T05:00:43.9705970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:43.9706099Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9706187Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:00:43.9706322Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:00:43.9706381Z         
2025-03-14T05:00:43.9706693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:43.9706854Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:00:43.9706953Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:00:43.9707078Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:00:43.9707164Z         
2025-03-14T05:00:43.9707480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:43.9707636Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:43.9707747Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:00:43.9707813Z         
2025-03-14T05:00:43.9708107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:43.9708263Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:43.9708371Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:00:43.9708438Z         
2025-03-14T05:00:43.9708734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:43.9708888Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:43.9708994Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:00:43.9709062Z         
2025-03-14T05:00:43.9709360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:43.9709546Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:00:43.9709654Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:00:43.9709723Z         
2025-03-14T05:00:43.9710055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:43.9710221Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:00:43.9710281Z         
2025-03-14T05:00:43.9710619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:43.9710749Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:00:43.9710817Z         
2025-03-14T05:00:43.9711160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:43.9711299Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:00:43.9711419Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:00:43.9711577Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:00:43.9711719Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:00:43.9711780Z         
2025-03-14T05:00:43.9712133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:43.9712263Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:00:43.9712408Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:00:43.9712556Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:00:43.9712696Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:00:43.9712782Z         
2025-03-14T05:00:43.9713136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:43.9713248Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:00:43.9713412Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:00:43.9713543Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:00:43.9713609Z         
2025-03-14T05:00:43.9713937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:43.9714052Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:00:43.9714219Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:00:43.9714357Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:00:43.9714420Z         
2025-03-14T05:00:43.9714740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:43.9714835Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:00:43.9714958Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:00:43.9715019Z         
2025-03-14T05:00:43.9715347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:43.9715440Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:00:43.9715575Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:00:43.9715636Z         
2025-03-14T05:00:43.9715947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:43.9716063Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:00:43.9716202Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:00:43.9716264Z         
2025-03-14T05:00:43.9716580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:43.9716691Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:00:43.9716823Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:00:43.9716884Z         
2025-03-14T05:00:43.9717236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:43.9717420Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:00:43.9717490Z         
2025-03-14T05:00:43.9717813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:43.9717978Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:00:43.9718038Z         
2025-03-14T05:00:43.9718435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:43.9718626Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:00:43.9718695Z         
2025-03-14T05:00:43.9719104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:43.9719312Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:00:43.9719381Z         
2025-03-14T05:00:43.9719803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:43.9719955Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:00:43.9720099Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:00:43.9720238Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:00:43.9720300Z         
2025-03-14T05:00:43.9720673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:43.9720833Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:00:43.9720899Z         
2025-03-14T05:00:43.9721204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:43.9721345Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:00:43.9721425Z         
2025-03-14T05:00:43.9721740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:43.9721867Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:00:43.9721992Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9722133Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:00:43.9722200Z         
2025-03-14T05:00:43.9722510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:43.9722636Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:00:43.9722752Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:00:43.9722905Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:00:43.9722968Z         
2025-03-14T05:00:43.9723279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:43.9723398Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:00:43.9723492Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:00:43.9723619Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:00:43.9723688Z         
2025-03-14T05:00:43.9723997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:43.9724168Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:00:43.9724257Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:00:43.9724408Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:00:43.9724468Z         
2025-03-14T05:00:43.9724789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:43.9724940Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:43.9725057Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:00:43.9725117Z         
2025-03-14T05:00:43.9725419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:43.9725568Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:43.9725683Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:00:43.9725745Z         
2025-03-14T05:00:43.9726048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:43.9726199Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:43.9726305Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:00:43.9726371Z         
2025-03-14T05:00:43.9726670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:43.9726856Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:00:43.9726960Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:00:43.9727047Z         
2025-03-14T05:00:43.9727380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:43.9727522Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:00:43.9727582Z         
2025-03-14T05:00:43.9727918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:43.9728048Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:00:43.9728116Z         
2025-03-14T05:00:43.9728458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:43.9728596Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:00:43.9728718Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:00:43.9728875Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:00:43.9729010Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:00:43.9729077Z         
2025-03-14T05:00:43.9729422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:43.9729563Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:00:43.9729701Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:00:43.9729857Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:00:43.9730007Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:00:43.9730075Z         
2025-03-14T05:00:43.9730421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:43.9730543Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:00:43.9730702Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:00:43.9730845Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:00:43.9730905Z         
2025-03-14T05:00:43.9731250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:43.9731427Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:00:43.9731625Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:00:43.9731760Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:00:43.9731834Z         
2025-03-14T05:00:43.9732162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:43.9732271Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:00:43.9732389Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:00:43.9732461Z         
2025-03-14T05:00:43.9732784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:43.9732883Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:00:43.9733015Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:00:43.9733084Z         
2025-03-14T05:00:43.9733386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:43.9733506Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:00:43.9733640Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:00:43.9733701Z         
2025-03-14T05:00:43.9734009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:43.9734120Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:00:43.9734252Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:00:43.9734314Z         
2025-03-14T05:00:43.9734665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:43.9734847Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:00:43.9734915Z         
2025-03-14T05:00:43.9735246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:43.9735409Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:00:43.9735468Z         
2025-03-14T05:00:43.9735871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:43.9736059Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:00:43.9736124Z         
2025-03-14T05:00:43.9736620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:00:43.9736760Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:00:43.9736819Z         
2025-03-14T05:00:43.9737118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9737256Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:00:43.9737324Z         
2025-03-14T05:00:43.9737758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:43.9737880Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:00:43.9737980Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:00:43.9738099Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:00:43.9738158Z         
2025-03-14T05:00:43.9738624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:43.9738755Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:43.9738994Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:00:43.9739073Z         
2025-03-14T05:00:43.9739535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:43.9739696Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:43.9739763Z         
2025-03-14T05:00:43.9740059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9740186Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:00:43.9740247Z         
2025-03-14T05:00:43.9740686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:43.9740808Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:00:43.9740914Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:00:43.9741036Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:00:43.9741096Z         
2025-03-14T05:00:43.9741571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:43.9741698Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:43.9741970Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:00:43.9742035Z         
2025-03-14T05:00:43.9742507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:43.9742683Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:43.9742752Z         
2025-03-14T05:00:43.9743041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9743171Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:00:43.9743231Z         
2025-03-14T05:00:43.9743664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:43.9743778Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:00:43.9743886Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:00:43.9744002Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:00:43.9744068Z         
2025-03-14T05:00:43.9744520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:43.9744657Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:43.9744892Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:00:43.9744960Z         
2025-03-14T05:00:43.9745409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:43.9745599Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:43.9745661Z         
2025-03-14T05:00:43.9745960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9746080Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:00:43.9746145Z         
2025-03-14T05:00:43.9746567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:43.9746683Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:00:43.9746792Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:00:43.9746905Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:00:43.9746975Z         
2025-03-14T05:00:43.9747432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:43.9747573Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:43.9747806Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:00:43.9747894Z         
2025-03-14T05:00:43.9748356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:43.9748555Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:43.9748635Z         
2025-03-14T05:00:43.9748931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9749051Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:00:43.9749121Z         
2025-03-14T05:00:43.9749553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:43.9749669Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:00:43.9749772Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:00:43.9749896Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:00:43.9749957Z         
2025-03-14T05:00:43.9750428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:43.9750593Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:00:43.9750836Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:00:43.9750898Z         
2025-03-14T05:00:43.9751367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:43.9751665Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:43.9751734Z         
2025-03-14T05:00:43.9752028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:43.9752160Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:00:43.9752223Z         
2025-03-14T05:00:43.9752513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:43.9752904Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:00:43.9752968Z         
2025-03-14T05:00:43.9753259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:43.9753729Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:00:43.9753799Z         
2025-03-14T05:00:43.9754078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:43.9754306Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:00:43.9754368Z         
2025-03-14T05:00:43.9754761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:00:43.9754920Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:00:43.9755007Z         
2025-03-14T05:00:43.9755307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:43.9755462Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:00:43.9755521Z         
2025-03-14T05:00:43.9755912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:00:43.9756045Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:00:43.9756116Z         
2025-03-14T05:00:43.9756609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:00:43.9756756Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:00:43.9756876Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:43.9757040Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:00:43.9757171Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:00:43.9757241Z         
2025-03-14T05:00:43.9757611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:00:43.9757732Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:00:43.9757824Z         
2025-03-14T05:00:44.9769178Z 
2025-03-14T05:00:44.9769703Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:44.9771322Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 269952, 4][1079808, 4, 1]cpu", L_anchors_0_tensor: "f32[269952, 4][4, 1]cpu", L_pred_anchor_deltas_1_: "f32[4, 67488, 4][269952, 4, 1]cpu", L_anchors_1_tensor: "f32[67488, 4][4, 1]cpu", L_pred_anchor_deltas_2_: "f32[4, 16872, 4][67488, 4, 1]cpu", L_anchors_2_tensor: "f32[16872, 4][4, 1]cpu", L_pred_anchor_deltas_3_: "f32[4, 4218, 4][16872, 4, 1]cpu", L_anchors_3_tensor: "f32[4218, 4][4, 1]cpu", L_pred_anchor_deltas_4_: "f32[4, 1083, 4][4332, 4, 1]cpu", L_anchors_4_tensor: "f32[1083, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 269952][269952, 1]cpu", L_pred_objectness_logits_1_: "f32[4, 67488][67488, 1]cpu", L_pred_objectness_logits_2_: "f32[4, 16872][16872, 1]cpu", L_pred_objectness_logits_3_: "f32[4, 4218][4218, 1]cpu", L_pred_objectness_logits_4_: "f32[4, 1083][1083, 1]cpu"):
2025-03-14T05:00:44.9773104Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T05:00:44.9773372Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T05:00:44.9773641Z         l_pred_anchor_deltas_1_ = L_pred_anchor_deltas_1_
2025-03-14T05:00:44.9773902Z         l_anchors_1_tensor = L_anchors_1_tensor
2025-03-14T05:00:44.9774160Z         l_pred_anchor_deltas_2_ = L_pred_anchor_deltas_2_
2025-03-14T05:00:44.9774407Z         l_anchors_2_tensor = L_anchors_2_tensor
2025-03-14T05:00:44.9774662Z         l_pred_anchor_deltas_3_ = L_pred_anchor_deltas_3_
2025-03-14T05:00:44.9774976Z         l_anchors_3_tensor = L_anchors_3_tensor
2025-03-14T05:00:44.9775215Z         l_pred_anchor_deltas_4_ = L_pred_anchor_deltas_4_
2025-03-14T05:00:44.9775446Z         l_anchors_4_tensor = L_anchors_4_tensor
2025-03-14T05:00:44.9775997Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T05:00:44.9776302Z         l_pred_objectness_logits_1_ = L_pred_objectness_logits_1_
2025-03-14T05:00:44.9776668Z         l_pred_objectness_logits_2_ = L_pred_objectness_logits_2_
2025-03-14T05:00:44.9776973Z         l_pred_objectness_logits_3_ = L_pred_objectness_logits_3_
2025-03-14T05:00:44.9777445Z         l_pred_objectness_logits_4_ = L_pred_objectness_logits_4_
2025-03-14T05:00:44.9777688Z         
2025-03-14T05:00:44.9778288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:44.9779002Z         pred_anchor_deltas_i: "f32[1079808, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T05:00:44.9779344Z         
2025-03-14T05:00:44.9779906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:44.9780611Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T05:00:44.9781023Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:00:44.9781382Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:00:44.9781650Z         
2025-03-14T05:00:44.9782134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:44.9782747Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T05:00:44.9783037Z         
2025-03-14T05:00:44.9783453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:44.9784009Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:00:44.9784275Z         
2025-03-14T05:00:44.9784676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:44.9785177Z         getitem: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:44.9785490Z         getitem_1: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:44.9785817Z         widths: "f32[1079808][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T05:00:44.9787411Z         
2025-03-14T05:00:44.9787931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:44.9788462Z         getitem_2: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:44.9788788Z         getitem_3: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:44.9789140Z         heights: "f32[1079808][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:00:44.9789442Z         
2025-03-14T05:00:44.9789842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:44.9790331Z         getitem_4: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:44.9790599Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:00:44.9791288Z         ctr_x: "f32[1079808][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T05:00:44.9791556Z         
2025-03-14T05:00:44.9792039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:44.9792582Z         getitem_5: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:44.9792933Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:00:44.9793250Z         ctr_y: "f32[1079808][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T05:00:44.9793507Z         
2025-03-14T05:00:44.9793942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:44.9794453Z         getitem_6: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:44.9794791Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T05:00:44.9795029Z         
2025-03-14T05:00:44.9795425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:44.9795955Z         getitem_7: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:44.9796301Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T05:00:44.9801199Z         
2025-03-14T05:00:44.9801792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:44.9802334Z         getitem_8: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:44.9802673Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:00:44.9802919Z         
2025-03-14T05:00:44.9803330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:44.9803899Z         getitem_9: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:44.9804361Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:00:44.9804614Z         
2025-03-14T05:00:44.9805061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:44.9805622Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:44.9805895Z         
2025-03-14T05:00:44.9806744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:44.9807376Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:44.9807658Z         
2025-03-14T05:00:44.9808142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:44.9808747Z         getitem_10: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:44.9809112Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T05:00:44.9809495Z         getitem_11: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:44.9809886Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T05:00:44.9810178Z         
2025-03-14T05:00:44.9810655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:44.9811291Z         getitem_12: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:44.9811750Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T05:00:44.9812141Z         getitem_13: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:44.9812577Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T05:00:44.9812909Z         
2025-03-14T05:00:44.9813388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:44.9813953Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:44.9814324Z         getitem_14: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:44.9814715Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T05:00:44.9815000Z         
2025-03-14T05:00:44.9815470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:44.9816523Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:44.9816887Z         getitem_15: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:44.9817269Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T05:00:44.9817535Z         
2025-03-14T05:00:44.9817962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:44.9818464Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:44.9818748Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:44.9819004Z         
2025-03-14T05:00:44.9819427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:44.9819957Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:44.9820240Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:44.9820495Z         
2025-03-14T05:00:44.9820910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:44.9821426Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:44.9821749Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:44.9822021Z         
2025-03-14T05:00:44.9822440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:44.9822953Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:44.9823925Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:44.9824205Z         
2025-03-14T05:00:44.9824676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:44.9825309Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:44.9825620Z         
2025-03-14T05:00:44.9826050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:44.9826640Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:00:44.9826923Z         
2025-03-14T05:00:44.9827415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:44.9830844Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:00:44.9831177Z         
2025-03-14T05:00:44.9831698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:44.9832390Z         pred_anchor_deltas_i_1: "f32[269952, 4][4, 1]cpu" = l_pred_anchor_deltas_1_.reshape(-1, 4);  l_pred_anchor_deltas_1_ = None
2025-03-14T05:00:44.9832729Z         
2025-03-14T05:00:44.9833270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:44.9833963Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = l_anchors_1_tensor.unsqueeze(0);  l_anchors_1_tensor = None
2025-03-14T05:00:44.9834366Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:00:44.9834727Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:00:44.9834995Z         
2025-03-14T05:00:44.9835466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:44.9836201Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:00:44.9836499Z         
2025-03-14T05:00:44.9836911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:44.9837464Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:00:44.9837743Z         
2025-03-14T05:00:44.9838159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:44.9838675Z         getitem_16: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:00:44.9839004Z         getitem_17: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:00:44.9839356Z         widths_1: "f32[269952][1]cpu" = getitem_16 - getitem_17;  getitem_16 = getitem_17 = None
2025-03-14T05:00:44.9839642Z         
2025-03-14T05:00:44.9840060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:44.9840582Z         getitem_18: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:00:44.9840893Z         getitem_19: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:00:44.9841236Z         heights_1: "f32[269952][1]cpu" = getitem_18 - getitem_19;  getitem_18 = getitem_19 = None
2025-03-14T05:00:44.9841517Z         
2025-03-14T05:00:44.9841917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:44.9842422Z         getitem_20: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:00:44.9842710Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:00:44.9843007Z         ctr_x_1: "f32[269952][1]cpu" = getitem_20 + mul_10;  getitem_20 = mul_10 = None
2025-03-14T05:00:44.9843279Z         
2025-03-14T05:00:44.9843714Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:44.9844245Z         getitem_21: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:00:44.9844600Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:00:44.9844908Z         ctr_y_1: "f32[269952][1]cpu" = getitem_21 + mul_11;  getitem_21 = mul_11 = None
2025-03-14T05:00:44.9845176Z         
2025-03-14T05:00:44.9845596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:44.9846128Z         getitem_22: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:44.9846472Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_22 / 1.0;  getitem_22 = None
2025-03-14T05:00:44.9846726Z         
2025-03-14T05:00:44.9847117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:44.9847646Z         getitem_23: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:44.9847988Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_23 / 1.0;  getitem_23 = None
2025-03-14T05:00:44.9848233Z         
2025-03-14T05:00:44.9848634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:44.9849161Z         getitem_24: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:44.9849489Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_24 / 1.0;  getitem_24 = None
2025-03-14T05:00:44.9849728Z         
2025-03-14T05:00:44.9850125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:44.9850678Z         getitem_25: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:00:44.9851061Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_25 / 1.0;  getitem_25 = None
2025-03-14T05:00:44.9851301Z         
2025-03-14T05:00:44.9851869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:44.9852431Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:00:44.9852708Z         
2025-03-14T05:00:44.9853125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:44.9853651Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:00:44.9853898Z         
2025-03-14T05:00:44.9854323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:44.9854858Z         getitem_26: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:00:44.9855181Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_26;  dx_1 = getitem_26 = None
2025-03-14T05:00:44.9855521Z         getitem_27: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:00:44.9855874Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_27;  mul_12 = getitem_27 = None
2025-03-14T05:00:44.9856134Z         
2025-03-14T05:00:44.9856562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:44.9857122Z         getitem_28: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:00:44.9857449Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_28;  dy_1 = getitem_28 = None
2025-03-14T05:00:44.9857808Z         getitem_29: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:00:44.9858175Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_29;  mul_13 = getitem_29 = None
2025-03-14T05:00:44.9859644Z         
2025-03-14T05:00:44.9860125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:44.9860877Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:00:44.9861221Z         getitem_30: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:00:44.9861588Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_30;  exp_2 = getitem_30 = None
2025-03-14T05:00:44.9861849Z         
2025-03-14T05:00:44.9862274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:44.9862979Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:00:44.9863477Z         getitem_31: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:00:44.9864009Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_31;  exp_3 = getitem_31 = None
2025-03-14T05:00:44.9864270Z         
2025-03-14T05:00:44.9864669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:44.9865131Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:00:44.9865404Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:00:44.9866081Z         
2025-03-14T05:00:44.9866828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:44.9867293Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:00:44.9867561Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:00:44.9867797Z         
2025-03-14T05:00:44.9868183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:44.9868659Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:00:44.9868963Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:00:44.9869215Z         
2025-03-14T05:00:44.9869606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:44.9870068Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:00:44.9870361Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:00:44.9870606Z         
2025-03-14T05:00:44.9871023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:44.9871609Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:00:44.9871922Z         
2025-03-14T05:00:44.9872365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:44.9872906Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:00:44.9873218Z         
2025-03-14T05:00:44.9873700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:44.9874297Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:00:44.9874580Z         
2025-03-14T05:00:44.9875040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:44.9875688Z         pred_anchor_deltas_i_2: "f32[67488, 4][4, 1]cpu" = l_pred_anchor_deltas_2_.reshape(-1, 4);  l_pred_anchor_deltas_2_ = None
2025-03-14T05:00:44.9876008Z         
2025-03-14T05:00:44.9876499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:44.9877162Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = l_anchors_2_tensor.unsqueeze(0);  l_anchors_2_tensor = None
2025-03-14T05:00:44.9877543Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:00:44.9877880Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:00:44.9878127Z         
2025-03-14T05:00:44.9878619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:44.9879203Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_2.float();  pred_anchor_deltas_i_2 = None
2025-03-14T05:00:44.9879479Z         
2025-03-14T05:00:44.9879859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:44.9880377Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:00:44.9880636Z         
2025-03-14T05:00:44.9881021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:44.9881501Z         getitem_32: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:00:44.9881799Z         getitem_33: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:00:44.9882121Z         widths_2: "f32[67488][1]cpu" = getitem_32 - getitem_33;  getitem_32 = getitem_33 = None
2025-03-14T05:00:44.9882381Z         
2025-03-14T05:00:44.9882797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:44.9883275Z         getitem_34: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:00:44.9883561Z         getitem_35: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:00:44.9883877Z         heights_2: "f32[67488][1]cpu" = getitem_34 - getitem_35;  getitem_34 = getitem_35 = None
2025-03-14T05:00:44.9884139Z         
2025-03-14T05:00:44.9884522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:44.9884994Z         getitem_36: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:00:44.9885260Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:00:44.9885554Z         ctr_x_2: "f32[67488][1]cpu" = getitem_36 + mul_20;  getitem_36 = mul_20 = None
2025-03-14T05:00:44.9885810Z         
2025-03-14T05:00:44.9886202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:44.9886735Z         getitem_37: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:00:44.9887049Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:00:44.9887329Z         ctr_y_2: "f32[67488][1]cpu" = getitem_37 + mul_21;  getitem_37 = mul_21 = None
2025-03-14T05:00:44.9887582Z         
2025-03-14T05:00:44.9887983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:44.9888490Z         getitem_38: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:44.9888814Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_38 / 1.0;  getitem_38 = None
2025-03-14T05:00:44.9889047Z         
2025-03-14T05:00:44.9889429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:44.9889935Z         getitem_39: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:44.9890255Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_39 / 1.0;  getitem_39 = None
2025-03-14T05:00:44.9890487Z         
2025-03-14T05:00:44.9891718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:44.9892312Z         getitem_40: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:44.9892653Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_40 / 1.0;  getitem_40 = None
2025-03-14T05:00:44.9892892Z         
2025-03-14T05:00:44.9893305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:44.9893905Z         getitem_41: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:00:44.9894252Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_41 / 1.0;  getitem_41 = None
2025-03-14T05:00:44.9894484Z         
2025-03-14T05:00:44.9894903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:44.9895437Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:00:44.9895707Z         
2025-03-14T05:00:44.9896121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:44.9896634Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:00:44.9896889Z         
2025-03-14T05:00:44.9897305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:44.9897836Z         getitem_42: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:00:44.9898152Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_42;  dx_2 = getitem_42 = None
2025-03-14T05:00:44.9898483Z         getitem_43: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:00:44.9898834Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_43;  mul_22 = getitem_43 = None
2025-03-14T05:00:44.9899090Z         
2025-03-14T05:00:44.9899584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:44.9900118Z         getitem_44: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:00:44.9900450Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_44;  dy_2 = getitem_44 = None
2025-03-14T05:00:44.9900796Z         getitem_45: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:00:44.9901140Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_45;  mul_23 = getitem_45 = None
2025-03-14T05:00:44.9901396Z         
2025-03-14T05:00:44.9901810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:44.9902311Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:00:44.9902640Z         getitem_46: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:00:44.9902999Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_46;  exp_4 = getitem_46 = None
2025-03-14T05:00:44.9903261Z         
2025-03-14T05:00:44.9903713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:44.9904249Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:00:44.9904582Z         getitem_47: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:00:44.9904948Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_47;  exp_5 = getitem_47 = None
2025-03-14T05:00:44.9905203Z         
2025-03-14T05:00:44.9905617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:44.9906136Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:00:44.9906405Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:00:44.9906665Z         
2025-03-14T05:00:44.9907073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:44.9907542Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:00:44.9907802Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:00:44.9908038Z         
2025-03-14T05:00:44.9908423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:44.9908902Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:00:44.9909204Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:00:44.9909457Z         
2025-03-14T05:00:44.9909842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:44.9910320Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:00:44.9910614Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:00:44.9910861Z         
2025-03-14T05:00:44.9911288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:44.9911872Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:00:44.9912167Z         
2025-03-14T05:00:44.9912603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:44.9913152Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:00:44.9913450Z         
2025-03-14T05:00:44.9913934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:44.9914565Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:00:44.9914853Z         
2025-03-14T05:00:44.9915333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:44.9916044Z         pred_anchor_deltas_i_3: "f32[16872, 4][4, 1]cpu" = l_pred_anchor_deltas_3_.reshape(-1, 4);  l_pred_anchor_deltas_3_ = None
2025-03-14T05:00:44.9916406Z         
2025-03-14T05:00:44.9916922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:44.9917615Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = l_anchors_3_tensor.unsqueeze(0);  l_anchors_3_tensor = None
2025-03-14T05:00:44.9918002Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:00:44.9918340Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:00:44.9918593Z         
2025-03-14T05:00:44.9919059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:44.9919660Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:00:44.9919967Z         
2025-03-14T05:00:44.9920362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:44.9920867Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:00:44.9921135Z         
2025-03-14T05:00:44.9921530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:44.9922022Z         getitem_48: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:00:44.9922327Z         getitem_49: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:00:44.9922654Z         widths_3: "f32[16872][1]cpu" = getitem_48 - getitem_49;  getitem_48 = getitem_49 = None
2025-03-14T05:00:44.9922922Z         
2025-03-14T05:00:44.9923317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:44.9923811Z         getitem_50: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:00:44.9924109Z         getitem_51: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:00:44.9924433Z         heights_3: "f32[16872][1]cpu" = getitem_50 - getitem_51;  getitem_50 = getitem_51 = None
2025-03-14T05:00:44.9924802Z         
2025-03-14T05:00:44.9925294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:44.9925865Z         getitem_52: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:00:44.9926437Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:00:44.9926793Z         ctr_x_3: "f32[16872][1]cpu" = getitem_52 + mul_30;  getitem_52 = mul_30 = None
2025-03-14T05:00:44.9927135Z         
2025-03-14T05:00:44.9927749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:44.9928339Z         getitem_53: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:00:44.9928725Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:00:44.9929054Z         ctr_y_3: "f32[16872][1]cpu" = getitem_53 + mul_31;  getitem_53 = mul_31 = None
2025-03-14T05:00:44.9940341Z         
2025-03-14T05:00:44.9940868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:44.9941439Z         getitem_54: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:44.9941793Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_54 / 1.0;  getitem_54 = None
2025-03-14T05:00:44.9942044Z         
2025-03-14T05:00:44.9942515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:44.9943052Z         getitem_55: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:44.9943393Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_55 / 1.0;  getitem_55 = None
2025-03-14T05:00:44.9943632Z         
2025-03-14T05:00:44.9944026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:44.9944552Z         getitem_56: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:44.9944876Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_56 / 1.0;  getitem_56 = None
2025-03-14T05:00:44.9945117Z         
2025-03-14T05:00:44.9945519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:44.9946176Z         getitem_57: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:00:44.9946541Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_57 / 1.0;  getitem_57 = None
2025-03-14T05:00:44.9946783Z         
2025-03-14T05:00:44.9947223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:44.9947771Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:00:44.9948046Z         
2025-03-14T05:00:44.9948461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:44.9948998Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:00:44.9949262Z         
2025-03-14T05:00:44.9949702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:44.9950257Z         getitem_58: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:00:44.9950587Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_58;  dx_3 = getitem_58 = None
2025-03-14T05:00:44.9950932Z         getitem_59: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:00:44.9951294Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_59;  mul_32 = getitem_59 = None
2025-03-14T05:00:44.9951594Z         
2025-03-14T05:00:44.9952039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:44.9952622Z         getitem_60: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:00:44.9952974Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_60;  dy_3 = getitem_60 = None
2025-03-14T05:00:44.9953319Z         getitem_61: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:00:44.9953676Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_61;  mul_33 = getitem_61 = None
2025-03-14T05:00:44.9953947Z         
2025-03-14T05:00:44.9954375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:44.9954890Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:00:44.9955231Z         getitem_62: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:00:44.9955605Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_62;  exp_6 = getitem_62 = None
2025-03-14T05:00:44.9955884Z         
2025-03-14T05:00:44.9956312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:44.9956827Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:00:44.9957171Z         getitem_63: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:00:44.9957535Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_63;  exp_7 = getitem_63 = None
2025-03-14T05:00:44.9957798Z         
2025-03-14T05:00:44.9958212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:44.9958683Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:00:44.9958982Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:00:44.9959231Z         
2025-03-14T05:00:44.9959633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:44.9960098Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:00:44.9960366Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:00:44.9960820Z         
2025-03-14T05:00:44.9961226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:44.9961719Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:00:44.9962036Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:00:44.9962301Z         
2025-03-14T05:00:44.9962704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:44.9963201Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:00:44.9963526Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:00:44.9963783Z         
2025-03-14T05:00:44.9964228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:44.9964913Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:00:44.9965226Z         
2025-03-14T05:00:44.9965659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:44.9966264Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:00:44.9966550Z         
2025-03-14T05:00:44.9967057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:44.9967676Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:00:44.9967970Z         
2025-03-14T05:00:44.9968459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:44.9969135Z         pred_anchor_deltas_i_4: "f32[4332, 4][4, 1]cpu" = l_pred_anchor_deltas_4_.reshape(-1, 4);  l_pred_anchor_deltas_4_ = None
2025-03-14T05:00:44.9969463Z         
2025-03-14T05:00:44.9970003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:44.9970694Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = l_anchors_4_tensor.unsqueeze(0);  l_anchors_4_tensor = None
2025-03-14T05:00:44.9971095Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:00:44.9971558Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:00:44.9971857Z         
2025-03-14T05:00:44.9972367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:44.9973006Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_4.float();  pred_anchor_deltas_i_4 = None
2025-03-14T05:00:44.9973372Z         
2025-03-14T05:00:44.9973774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:44.9974291Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:00:44.9974554Z         
2025-03-14T05:00:44.9974957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:44.9975463Z         getitem_64: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:00:44.9975775Z         getitem_65: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:00:44.9976106Z         widths_4: "f32[4332][1]cpu" = getitem_64 - getitem_65;  getitem_64 = getitem_65 = None
2025-03-14T05:00:44.9976381Z         
2025-03-14T05:00:44.9976785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:44.9977290Z         getitem_66: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:00:44.9977590Z         getitem_67: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:00:44.9977924Z         heights_4: "f32[4332][1]cpu" = getitem_66 - getitem_67;  getitem_66 = getitem_67 = None
2025-03-14T05:00:44.9978196Z         
2025-03-14T05:00:44.9978598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:44.9979107Z         getitem_68: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:00:44.9979380Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:00:44.9979662Z         ctr_x_4: "f32[4332][1]cpu" = getitem_68 + mul_40;  getitem_68 = mul_40 = None
2025-03-14T05:00:44.9979940Z         
2025-03-14T05:00:44.9980365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:44.9980889Z         getitem_69: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:00:44.9981184Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:00:44.9981456Z         ctr_y_4: "f32[4332][1]cpu" = getitem_69 + mul_41;  getitem_69 = mul_41 = None
2025-03-14T05:00:44.9981707Z         
2025-03-14T05:00:44.9982105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:44.9982627Z         getitem_70: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:44.9982955Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_70 / 1.0;  getitem_70 = None
2025-03-14T05:00:44.9983196Z         
2025-03-14T05:00:44.9983588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:44.9984106Z         getitem_71: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:44.9984431Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_71 / 1.0;  getitem_71 = None
2025-03-14T05:00:44.9984666Z         
2025-03-14T05:00:44.9985054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:44.9985566Z         getitem_72: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:44.9985887Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_72 / 1.0;  getitem_72 = None
2025-03-14T05:00:44.9986120Z         
2025-03-14T05:00:44.9986529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:44.9987084Z         getitem_73: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:00:44.9987436Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_73 / 1.0;  getitem_73 = None
2025-03-14T05:00:44.9987674Z         
2025-03-14T05:00:44.9988111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:44.9988649Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:00:44.9988909Z         
2025-03-14T05:00:44.9989327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:44.9989861Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:00:44.9990121Z         
2025-03-14T05:00:44.9990556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:44.9991100Z         getitem_74: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:00:44.9991423Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_74;  dx_4 = getitem_74 = None
2025-03-14T05:00:44.9991763Z         getitem_75: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:00:44.9992139Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_75;  mul_42 = getitem_75 = None
2025-03-14T05:00:44.9992406Z         
2025-03-14T05:00:44.9992859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:44.9993438Z         getitem_76: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:00:44.9993779Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_76;  dy_4 = getitem_76 = None
2025-03-14T05:00:44.9994118Z         getitem_77: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:00:44.9994475Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_77;  mul_43 = getitem_77 = None
2025-03-14T05:00:44.9994728Z         
2025-03-14T05:00:44.9995146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:44.9995649Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:00:44.9995975Z         getitem_78: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:00:44.9996332Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_78;  exp_8 = getitem_78 = None
2025-03-14T05:00:44.9996589Z         
2025-03-14T05:00:44.9997023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:44.9997542Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:00:44.9997884Z         getitem_79: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:00:44.9998257Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_79;  exp_9 = getitem_79 = None
2025-03-14T05:00:44.9998507Z         
2025-03-14T05:00:44.9998899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:44.9999375Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:00:44.9999642Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:00:44.9999881Z         
2025-03-14T05:00:45.0000282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:45.0000739Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:00:45.0000992Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:00:45.0001221Z         
2025-03-14T05:00:45.0001605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:45.0002071Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:00:45.0002368Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:00:45.0002618Z         
2025-03-14T05:00:45.0003001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:45.0003469Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:00:45.0003767Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:00:45.0004018Z         
2025-03-14T05:00:45.0004459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:45.0005053Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:00:45.0005353Z         
2025-03-14T05:00:45.0005788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:45.0006383Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:00:45.0006695Z         
2025-03-14T05:00:45.0007187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:45.0007823Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:00:45.0008121Z         
2025-03-14T05:00:45.0008735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:00:45.0009498Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:00:45.0009780Z         
2025-03-14T05:00:45.0010178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:45.0010681Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:00:45.0010949Z         
2025-03-14T05:00:45.0011630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:45.0012375Z         topk = l_pred_objectness_logits_0_.topk(1000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T05:00:45.0012745Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:00:45.0013047Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:00:45.0013330Z         
2025-03-14T05:00:45.0013980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:45.0014702Z         getitem_82: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:45.0015164Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_82, topk_idx)];  proposals_i_5 = getitem_82 = topk_idx = None
2025-03-14T05:00:45.0015541Z         
2025-03-14T05:00:45.0016148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:45.0016903Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:45.0017218Z         
2025-03-14T05:00:45.0017642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:45.0018160Z         to_6: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:00:45.0018423Z         
2025-03-14T05:00:45.0018997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:45.0019727Z         topk_1 = l_pred_objectness_logits_1_.topk(1000, dim = 1);  l_pred_objectness_logits_1_ = None
2025-03-14T05:00:45.0020065Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:00:45.0020372Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:00:45.0020611Z         
2025-03-14T05:00:45.0021160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:45.0021865Z         getitem_86: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:45.0022292Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_86, topk_idx_1)];  proposals_i_6 = getitem_86 = topk_idx_1 = None
2025-03-14T05:00:45.0022646Z         
2025-03-14T05:00:45.0023206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:45.0023901Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:45.0024194Z         
2025-03-14T05:00:45.0024588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:45.0025087Z         to_7: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:00:45.0025342Z         
2025-03-14T05:00:45.0025881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:45.0026567Z         topk_2 = l_pred_objectness_logits_2_.topk(1000, dim = 1);  l_pred_objectness_logits_2_ = None
2025-03-14T05:00:45.0026912Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:00:45.0027205Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:00:45.0027456Z         
2025-03-14T05:00:45.0028041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:45.0028746Z         getitem_90: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:45.0029175Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_90, topk_idx_2)];  proposals_i_7 = getitem_90 = topk_idx_2 = None
2025-03-14T05:00:45.0029527Z         
2025-03-14T05:00:45.0030078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:45.0030764Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:45.0031050Z         
2025-03-14T05:00:45.0031438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:45.0031928Z         to_8: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:00:45.0032172Z         
2025-03-14T05:00:45.0032700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:45.0033372Z         topk_3 = l_pred_objectness_logits_3_.topk(1000, dim = 1);  l_pred_objectness_logits_3_ = None
2025-03-14T05:00:45.0033708Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:00:45.0033988Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:00:45.0034234Z         
2025-03-14T05:00:45.0034805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:45.0035474Z         getitem_94: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:00:45.0035919Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_94, topk_idx_3)];  proposals_i_8 = getitem_94 = topk_idx_3 = None
2025-03-14T05:00:45.0036271Z         
2025-03-14T05:00:45.0036818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:45.0037496Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:45.0037782Z         
2025-03-14T05:00:45.0038165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:45.0038654Z         to_9: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:00:45.0038890Z         
2025-03-14T05:00:45.0039401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:45.0040059Z         topk_4 = l_pred_objectness_logits_4_.topk(1000, dim = 1);  l_pred_objectness_logits_4_ = None
2025-03-14T05:00:45.0040401Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:00:45.0040672Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:00:45.0040903Z         
2025-03-14T05:00:45.0041442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:45.0042132Z         getitem_98: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:00:45.0042569Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_98, topk_idx_4)];  proposals_i_9 = getitem_98 = topk_idx_4 = None
2025-03-14T05:00:45.0042907Z         
2025-03-14T05:00:45.0043438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:45.0044117Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:45.0044414Z         
2025-03-14T05:00:45.0044810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:45.0045295Z         to_10: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:00:45.0045546Z         
2025-03-14T05:00:45.0045920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:45.0046643Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:00:45.0047134Z         
2025-03-14T05:00:45.0047507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:45.0048330Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:00:45.0048935Z         
2025-03-14T05:00:45.0049321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:45.0049858Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_6, to_7, to_8, to_9, to_10], 0);  to_6 = to_7 = to_8 = to_9 = to_10 = level_ids = None
2025-03-14T05:00:45.0050163Z         
2025-03-14T05:00:45.0050654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:00:45.0051263Z         getitem_100: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:00:45.0051663Z         
2025-03-14T05:00:45.0052096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:45.0052665Z         tensor: "f32[5000, 4][4, 1]cpu" = getitem_100.to(torch.float32);  getitem_100 = None
2025-03-14T05:00:45.0052940Z         
2025-03-14T05:00:45.0053419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:00:45.0053994Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:00:45.0054250Z         
2025-03-14T05:00:45.0054834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:00:45.0055527Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T05:00:45.0055842Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:45.0056209Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:00:45.0056558Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:00:45.0056813Z         
2025-03-14T05:00:45.0057273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:00:45.0057818Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:00:45.0058051Z         
2025-03-14T05:00:51.1797159Z 
2025-03-14T05:00:51.1797918Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:51.1800444Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:00:51.1802578Z         l_stack0_ = L_stack0_
2025-03-14T05:00:51.1802934Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:00:51.1803498Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:00:51.1804035Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:00:51.1804592Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:00:51.1805123Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:00:51.1805693Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:00:51.1806264Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:00:51.1806832Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:00:51.1807324Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:00:51.1807739Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:00:51.1808151Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:00:51.1808537Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:00:51.1808824Z         
2025-03-14T05:00:51.1809238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:00:51.1809746Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:00:51.1810447Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:00:51.1811208Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:00:51.1812021Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:00:51.1812741Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:00:51.1813011Z         
2025-03-14T05:00:51.1813442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:00:51.1814453Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:00:51.1815158Z         
2025-03-14T05:00:51.1815570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:00:51.1816716Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:00:51.1817600Z         
2025-03-14T05:00:51.1817998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.1818467Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.1818729Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:00:51.1818967Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:00:51.1819249Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.1819501Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:00:51.1819746Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:00:51.1820027Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.1820281Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:00:51.1820520Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:00:51.1820792Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.1821045Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:00:51.1821276Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:00:51.1821499Z         
2025-03-14T05:00:51.1821873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:51.1822647Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:00:51.1823193Z         
2025-03-14T05:00:51.1823656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:51.1824258Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:00:51.1824531Z         
2025-03-14T05:00:51.1824927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:51.1825461Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:00:51.1825747Z         
2025-03-14T05:00:51.1826132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:51.1826622Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:51.1826930Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.1827247Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:00:51.1827507Z         
2025-03-14T05:00:51.1827900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:51.1828397Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:51.1828709Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:51.1829039Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:00:51.1829308Z         
2025-03-14T05:00:51.1829719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:51.1830218Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.1830496Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:00:51.1830786Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:00:51.1831033Z         
2025-03-14T05:00:51.1831451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:51.1831962Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:51.1832271Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:00:51.1832555Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:00:51.1832804Z         
2025-03-14T05:00:51.1833226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:51.1833747Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:51.1834062Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:00:51.1834281Z         
2025-03-14T05:00:51.1834653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:51.1835150Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:51.1835457Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:00:51.1835680Z         
2025-03-14T05:00:51.1836051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:51.1836555Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:51.1836898Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:00:51.1837129Z         
2025-03-14T05:00:51.1837514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:51.1838045Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:51.1838401Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:00:51.1838621Z         
2025-03-14T05:00:51.1839027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:51.1839537Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:51.1839781Z         
2025-03-14T05:00:51.1840181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:51.1840683Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:51.1840926Z         
2025-03-14T05:00:51.1841343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:51.1841870Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:51.1842180Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:00:51.1842517Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:51.1842853Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:00:51.1843125Z         
2025-03-14T05:00:51.1843582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:51.1844102Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:51.1844406Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:00:51.1844730Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:51.1845060Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:00:51.1845309Z         
2025-03-14T05:00:51.1845720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:51.1846217Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:51.1846540Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:51.1846879Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:00:51.1847125Z         
2025-03-14T05:00:51.1847537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:51.1848029Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:51.1848363Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:51.1848711Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:00:51.1848981Z         
2025-03-14T05:00:51.1849383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:51.1849843Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:51.1850102Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:51.1850337Z         
2025-03-14T05:00:51.1850727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:51.1851185Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:51.1851563Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:51.1851815Z         
2025-03-14T05:00:51.1852220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:51.1852730Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:51.1853022Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:51.1853275Z         
2025-03-14T05:00:51.1853675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:51.1854158Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:51.1854450Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:51.1854705Z         
2025-03-14T05:00:51.1855160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:51.1855742Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:51.1856053Z         
2025-03-14T05:00:51.1856489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:51.1857048Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:00:51.1857336Z         
2025-03-14T05:00:51.1857791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:00:51.1858470Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:00:51.1858900Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:00:51.1859188Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:00:51.1859491Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:00:51.1859794Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:00:51.1860044Z         
2025-03-14T05:00:51.1860422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.1861163Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.1861514Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:00:51.1861755Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:00:51.1862124Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.1862462Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:00:51.1862757Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:00:51.1863116Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.1863474Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:00:51.1863708Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:00:51.1864061Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.1864393Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:00:51.1864619Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:00:51.1864834Z         
2025-03-14T05:00:51.1865260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:00:51.1865812Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:00:51.1866099Z         
2025-03-14T05:00:51.1866537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:00:51.1867198Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:00:51.1867608Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:00:51.1867888Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:00:51.1868178Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:00:51.1868500Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:00:51.1868748Z         
2025-03-14T05:00:51.1869301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:51.1870039Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:00:51.1870376Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:51.1870704Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:00:51.1871035Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:51.1871320Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:51.1871553Z         
2025-03-14T05:00:51.1871987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:51.1872504Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:51.1872736Z         
2025-03-14T05:00:51.1903615Z 
2025-03-14T05:00:51.1904189Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:51.1906578Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:00:51.1908841Z         l_stack0_ = L_stack0_
2025-03-14T05:00:51.1909202Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:00:51.1909695Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:00:51.1910183Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:00:51.1910663Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:00:51.1911193Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:00:51.1911788Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:00:51.1912366Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:00:51.1912939Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:00:51.1913430Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:00:51.1913847Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:00:51.1914290Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:00:51.1914704Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:00:51.1915042Z         
2025-03-14T05:00:51.1915479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:00:51.1915997Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:00:51.1916752Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:00:51.1917474Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:00:51.1918194Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:00:51.1918906Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:00:51.1919185Z         
2025-03-14T05:00:51.1919595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:00:51.1920566Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:00:51.1921277Z         
2025-03-14T05:00:51.1921687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:00:51.1922672Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:00:51.1923374Z         
2025-03-14T05:00:51.1923734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.1924178Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.1924421Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:00:51.1924645Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:00:51.1924909Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.1925151Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:00:51.1925384Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:00:51.1925646Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.1925882Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:00:51.1926104Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:00:51.1926359Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.1926594Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:00:51.1926815Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:00:51.1927028Z         
2025-03-14T05:00:51.1927410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:51.1928178Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:00:51.1928766Z         
2025-03-14T05:00:51.1929241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:51.1929816Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:00:51.1930109Z         
2025-03-14T05:00:51.1930544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:51.1931077Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:00:51.1931359Z         
2025-03-14T05:00:51.1931885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:51.1932448Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:51.1932801Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.1933162Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:00:51.1933446Z         
2025-03-14T05:00:51.1933898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:51.1934398Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:51.1934710Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:51.1935062Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:00:51.1935375Z         
2025-03-14T05:00:51.1935794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:51.1936306Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.1936603Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:00:51.1936893Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:00:51.1937152Z         
2025-03-14T05:00:51.1937570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:51.1938114Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:51.1939484Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:00:51.1939859Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:00:51.1940135Z         
2025-03-14T05:00:51.1940594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:51.1941146Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:51.1941490Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:00:51.1941733Z         
2025-03-14T05:00:51.1942188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:51.1942730Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:51.1943065Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:00:51.1943331Z         
2025-03-14T05:00:51.1943763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:51.1944277Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:51.1944587Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:00:51.1944811Z         
2025-03-14T05:00:51.1945193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:51.1945719Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:51.1946054Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:00:51.1946279Z         
2025-03-14T05:00:51.1946697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:51.1947228Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:51.1947476Z         
2025-03-14T05:00:51.1947896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:51.1948395Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:51.1948636Z         
2025-03-14T05:00:51.1949060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:51.1949628Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:51.1949942Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:00:51.1950270Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:51.1950605Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:00:51.1950854Z         
2025-03-14T05:00:51.1951274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:51.1951797Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:51.1952111Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:00:51.1952432Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:51.1952768Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:00:51.1953012Z         
2025-03-14T05:00:51.1953416Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:51.1953905Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:51.1954227Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:51.1954562Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:00:51.1954828Z         
2025-03-14T05:00:51.1955231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:51.1955740Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:51.1956084Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:51.1956424Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:00:51.1956671Z         
2025-03-14T05:00:51.1957063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:51.1957519Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:51.1957769Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:51.1957996Z         
2025-03-14T05:00:51.1958374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:51.1958819Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:51.1959071Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:51.1959294Z         
2025-03-14T05:00:51.1959664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:51.1960122Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:51.1960405Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:51.1960842Z         
2025-03-14T05:00:51.1961232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:51.1961692Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:51.1962040Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:51.1962277Z         
2025-03-14T05:00:51.1962707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:51.1963273Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:51.1963554Z         
2025-03-14T05:00:51.1963963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:51.1964508Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:00:51.1964783Z         
2025-03-14T05:00:51.1965231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:00:51.1965914Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:00:51.1966337Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:00:51.1966620Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:00:51.1966914Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:00:51.1967215Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:00:51.1967464Z         
2025-03-14T05:00:51.1967875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.1968432Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.1968806Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:00:51.1969048Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:00:51.1969445Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.1969794Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:00:51.1970029Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:00:51.1970397Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.1970735Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:00:51.1970969Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:00:51.1971333Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.1971813Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:00:51.1972093Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:00:51.1972388Z         
2025-03-14T05:00:51.1972815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:00:51.1973376Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:00:51.1973721Z         
2025-03-14T05:00:51.1974147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:00:51.1974794Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:00:51.1975193Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:00:51.1975494Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:00:51.1975774Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:00:51.1976070Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:00:51.1976310Z         
2025-03-14T05:00:51.1976844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:51.1977542Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:00:51.1977987Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:51.1978444Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:00:51.1978922Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:51.1979231Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:51.1979483Z         
2025-03-14T05:00:51.1980136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:51.1980685Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:51.1980910Z         
2025-03-14T05:00:51.1985118Z 
2025-03-14T05:00:51.1985480Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:51.1987654Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:00:51.1989690Z         l_stack0_ = L_stack0_
2025-03-14T05:00:51.1990038Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:00:51.1990517Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:00:51.1990987Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:00:51.1991447Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:00:51.1991949Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:00:51.1992501Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:00:51.1993053Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:00:51.1993603Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:00:51.1994111Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:00:51.1994501Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:00:51.1994884Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:00:51.1995261Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:00:51.1995546Z         
2025-03-14T05:00:51.1995934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:00:51.1996406Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:00:51.1997098Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:00:51.1997800Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:00:51.1998501Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:00:51.1999202Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:00:51.1999476Z         
2025-03-14T05:00:51.1999899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:00:51.2000893Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:00:51.2001599Z         
2025-03-14T05:00:51.2001998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:00:51.2002958Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:00:51.2003665Z         
2025-03-14T05:00:51.2004030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.2004484Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.2004730Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:00:51.2004955Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:00:51.2005264Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.2005510Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:00:51.2005743Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:00:51.2006003Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.2006246Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:00:51.2006480Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:00:51.2006739Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.2007007Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:00:51.2007231Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:00:51.2007440Z         
2025-03-14T05:00:51.2007810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:51.2008577Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:00:51.2009142Z         
2025-03-14T05:00:51.2009624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:51.2010224Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:00:51.2010508Z         
2025-03-14T05:00:51.2010920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:51.2011636Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:00:51.2011967Z         
2025-03-14T05:00:51.2012431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:51.2012993Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:51.2013340Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.2013714Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:00:51.2013998Z         
2025-03-14T05:00:51.2014427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:51.2014996Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:51.2015325Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:51.2015679Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:00:51.2015963Z         
2025-03-14T05:00:51.2016375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:51.2016891Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.2017185Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:00:51.2017469Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:00:51.2017730Z         
2025-03-14T05:00:51.2018146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:51.2018683Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:51.2018998Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:00:51.2019294Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:00:51.2019548Z         
2025-03-14T05:00:51.2019971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:51.2020508Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:51.2020872Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:00:51.2021116Z         
2025-03-14T05:00:51.2021519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:51.2022044Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:51.2022383Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:00:51.2022628Z         
2025-03-14T05:00:51.2022996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:51.2023480Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:51.2023787Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:00:51.2024004Z         
2025-03-14T05:00:51.2024383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:51.2024903Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:51.2025238Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:00:51.2025460Z         
2025-03-14T05:00:51.2025872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:51.2026382Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:51.2026648Z         
2025-03-14T05:00:51.2027052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:51.2027579Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:51.2027831Z         
2025-03-14T05:00:51.2028274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:51.2028816Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:51.2029143Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:00:51.2029465Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:51.2029802Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:00:51.2030046Z         
2025-03-14T05:00:51.2030472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:51.2030998Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:51.2031303Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:00:51.2031621Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:51.2031949Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:00:51.2032189Z         
2025-03-14T05:00:51.2032594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:51.2033078Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:51.2033435Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:51.2033776Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:00:51.2034027Z         
2025-03-14T05:00:51.2034444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:51.2034937Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:51.2035269Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:51.2035616Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:00:51.2035869Z         
2025-03-14T05:00:51.2036264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:51.2036718Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:51.2036981Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:51.2037218Z         
2025-03-14T05:00:51.2037609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:51.2038059Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:51.2038318Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:51.2038546Z         
2025-03-14T05:00:51.2039694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:51.2040207Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:51.2040497Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:51.2040772Z         
2025-03-14T05:00:51.2041180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:51.2041654Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:51.2041925Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:51.2042157Z         
2025-03-14T05:00:51.2042578Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:51.2043137Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:51.2043413Z         
2025-03-14T05:00:51.2043820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:51.2044359Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:00:51.2044634Z         
2025-03-14T05:00:51.2045066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:00:51.2045717Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:00:51.2046129Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:00:51.2046405Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:00:51.2046690Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:00:51.2047703Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:00:51.2047958Z         
2025-03-14T05:00:51.2048342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.2049015Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.2049502Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:00:51.2049820Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:00:51.2050288Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.2050796Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:00:51.2051032Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:00:51.2051484Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.2051990Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:00:51.2052238Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:00:51.2052618Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.2052966Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:00:51.2053195Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:00:51.2053410Z         
2025-03-14T05:00:51.2053835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:00:51.2054419Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:00:51.2054700Z         
2025-03-14T05:00:51.2055133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:00:51.2055824Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:00:51.2056234Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:00:51.2056515Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:00:51.2056810Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:00:51.2057114Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:00:51.2057366Z         
2025-03-14T05:00:51.2057922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:51.2058612Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:00:51.2058948Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:51.2059282Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:00:51.2059616Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:51.2059899Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:51.2060139Z         
2025-03-14T05:00:51.2060720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:51.2061252Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:51.2061486Z         
2025-03-14T05:00:51.2061710Z 
2025-03-14T05:00:51.2061808Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:51.2063690Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:00:51.2065691Z         l_stack0_ = L_stack0_
2025-03-14T05:00:51.2066039Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:00:51.2066518Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:00:51.2066991Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:00:51.2067458Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:00:51.2068079Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:00:51.2068660Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:00:51.2069278Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:00:51.2069862Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:00:51.2070331Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:00:51.2070720Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:00:51.2071100Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:00:51.2071479Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:00:51.2071760Z         
2025-03-14T05:00:51.2072122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:00:51.2072580Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:00:51.2073264Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:00:51.2073961Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:00:51.2074663Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:00:51.2075381Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:00:51.2075651Z         
2025-03-14T05:00:51.2076035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:00:51.2076991Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:00:51.2077713Z         
2025-03-14T05:00:51.2078130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:00:51.2079108Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:00:51.2079834Z         
2025-03-14T05:00:51.2080203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.2080652Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.2080902Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:00:51.2081133Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:00:51.2081421Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.2081663Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:00:51.2081888Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:00:51.2082165Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.2082401Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:00:51.2082654Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:00:51.2082913Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.2083150Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:00:51.2083369Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:00:51.2083577Z         
2025-03-14T05:00:51.2083949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:51.2084716Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:00:51.2085256Z         
2025-03-14T05:00:51.2085710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:51.2086280Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:00:51.2086547Z         
2025-03-14T05:00:51.2086937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:51.2087451Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:00:51.2087722Z         
2025-03-14T05:00:51.2088117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:51.2088651Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:51.2088985Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.2089331Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:00:51.2089606Z         
2025-03-14T05:00:51.2090028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:51.2090581Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:51.2090921Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:51.2091294Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:00:51.2091760Z         
2025-03-14T05:00:51.2092202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:51.2092756Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.2093068Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:00:51.2093363Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:00:51.2093609Z         
2025-03-14T05:00:51.2094004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:51.2094564Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:51.2094868Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:00:51.2095158Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:00:51.2095442Z         
2025-03-14T05:00:51.2095890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:51.2096405Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:51.2096730Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:00:51.2096960Z         
2025-03-14T05:00:51.2097343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:51.2097848Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:51.2098165Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:00:51.2098391Z         
2025-03-14T05:00:51.2098770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:51.2100317Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:51.2100665Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:00:51.2100914Z         
2025-03-14T05:00:51.2101337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:51.2101917Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:51.2102265Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:00:51.2102507Z         
2025-03-14T05:00:51.2102953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:51.2103546Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:51.2103812Z         
2025-03-14T05:00:51.2104253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:51.2104801Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:51.2105065Z         
2025-03-14T05:00:51.2105538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:51.2106094Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:51.2106420Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:00:51.2106756Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:51.2107107Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:00:51.2107367Z         
2025-03-14T05:00:51.2107811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:51.2108363Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:51.2108684Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:00:51.2109038Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:51.2109387Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:00:51.2109665Z         
2025-03-14T05:00:51.2110107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:51.2110621Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:51.2110952Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:51.2111299Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:00:51.2111553Z         
2025-03-14T05:00:51.2111981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:51.2112487Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:51.2112827Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:51.2113182Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:00:51.2113440Z         
2025-03-14T05:00:51.2113847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:51.2114315Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:51.2114583Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:51.2114825Z         
2025-03-14T05:00:51.2115228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:51.2115697Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:51.2115970Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:51.2116202Z         
2025-03-14T05:00:51.2116589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:51.2117060Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:51.2117349Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:51.2117583Z         
2025-03-14T05:00:51.2117970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:51.2118435Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:51.2118719Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:51.2118960Z         
2025-03-14T05:00:51.2119390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:51.2119967Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:51.2120251Z         
2025-03-14T05:00:51.2120673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:51.2121222Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:00:51.2121504Z         
2025-03-14T05:00:51.2121965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:00:51.2122647Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:00:51.2123088Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:00:51.2123391Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:00:51.2123692Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:00:51.2124009Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:00:51.2124263Z         
2025-03-14T05:00:51.2124674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.2125297Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.2125666Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:00:51.2125912Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:00:51.2126299Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.2126702Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:00:51.2126946Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:00:51.2127330Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.2127690Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:00:51.2127922Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:00:51.2128274Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.2128610Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:00:51.2128836Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:00:51.2129071Z         
2025-03-14T05:00:51.2129490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:00:51.2130053Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:00:51.2130338Z         
2025-03-14T05:00:51.2130779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:00:51.2131529Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:00:51.2131972Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:00:51.2132271Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:00:51.2132579Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:00:51.2132902Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:00:51.2133151Z         
2025-03-14T05:00:51.2133695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:51.2134391Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:00:51.2134720Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:51.2135037Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:00:51.2135388Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:51.2135670Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:51.2135941Z         
2025-03-14T05:00:51.2136393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:51.2136920Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:51.2137158Z         
2025-03-14T05:00:51.6508907Z 
2025-03-14T05:00:51.6511354Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:51.6512440Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:00:51.6515701Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:00:51.6516155Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:00:51.6516540Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:00:51.6517030Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:00:51.6517447Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:00:51.6517845Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:00:51.6518158Z         
2025-03-14T05:00:51.6518634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.6519180Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.6519443Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:00:51.6519685Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:00:51.6520252Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.6520519Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:00:51.6520770Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:00:51.6521053Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.6521314Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:00:51.6521556Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:00:51.6521829Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.6522083Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:00:51.6522328Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:00:51.6522620Z         
2025-03-14T05:00:51.6523046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:51.6523961Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:00:51.6524693Z         
2025-03-14T05:00:51.6525167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:51.6525754Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:00:51.6526031Z         
2025-03-14T05:00:51.6526503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:51.6527039Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:00:51.6527323Z         
2025-03-14T05:00:51.6527787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:51.6528330Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:51.6528654Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.6528995Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:00:51.6529267Z         
2025-03-14T05:00:51.6529677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:51.6530188Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:51.6530503Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:51.6530847Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:00:51.6531130Z         
2025-03-14T05:00:51.6531774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:51.6532289Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.6532576Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:00:51.6532866Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:00:51.6533110Z         
2025-03-14T05:00:51.6533509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:51.6534026Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:51.6534358Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:00:51.6534637Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:00:51.6534885Z         
2025-03-14T05:00:51.6535300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:51.6535804Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:51.6536130Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:00:51.6536363Z         
2025-03-14T05:00:51.6536749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:51.6537248Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:51.6537567Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:00:51.6537799Z         
2025-03-14T05:00:51.6538184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:51.6538682Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:51.6538999Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:00:51.6539228Z         
2025-03-14T05:00:51.6539631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:51.6540163Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:51.6540531Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:00:51.6540763Z         
2025-03-14T05:00:51.6541202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:51.6541727Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:51.6541981Z         
2025-03-14T05:00:51.6542392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:51.6542904Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:51.6543160Z         
2025-03-14T05:00:51.6543566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:51.6544102Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:51.6544409Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:00:51.6544732Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:51.6545068Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:00:51.6545313Z         
2025-03-14T05:00:51.6545731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:51.6546262Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:51.6546571Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:00:51.6546914Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:51.6547250Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:00:51.6547506Z         
2025-03-14T05:00:51.6547921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:51.6548419Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:51.6548748Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:51.6549095Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:00:51.6549335Z         
2025-03-14T05:00:51.6549738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:51.6550226Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:51.6550547Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:51.6550884Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:00:51.6551126Z         
2025-03-14T05:00:51.6551508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:51.6551945Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:51.6552211Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:51.6552439Z         
2025-03-14T05:00:51.6552819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:51.6553276Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:51.6553542Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:51.6553768Z         
2025-03-14T05:00:51.6554143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:51.6554603Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:51.6554886Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:51.6555124Z         
2025-03-14T05:00:51.6555528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:51.6555998Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:51.6556282Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:51.6556519Z         
2025-03-14T05:00:51.6556945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:51.6557513Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:51.6557799Z         
2025-03-14T05:00:51.6558215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:51.6558768Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:00:51.6559050Z         
2025-03-14T05:00:51.6559509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:00:51.6560174Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:00:51.6560757Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:00:51.6561048Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:00:51.6561337Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:00:51.6561633Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:00:51.6561877Z         
2025-03-14T05:00:51.6562243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.6562780Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.6563113Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:00:51.6563349Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:00:51.6563697Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.6564023Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:00:51.6564249Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:00:51.6564595Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.6564915Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:00:51.6565207Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:00:51.6565553Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.6565946Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:00:51.6566177Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:00:51.6566398Z         
2025-03-14T05:00:51.6566842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:00:51.6567449Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:00:51.6567779Z         
2025-03-14T05:00:51.6568234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:00:51.6568927Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:00:51.6569359Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:00:51.6569656Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:00:51.6569960Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:00:51.6570286Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:00:51.6570554Z         
2025-03-14T05:00:51.6571133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:51.6571914Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:00:51.6572262Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:51.6572607Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:00:51.6572987Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:51.6573285Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:51.6573517Z         
2025-03-14T05:00:51.6573948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:51.6574462Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:51.6574705Z         
2025-03-14T05:00:51.6583276Z 
2025-03-14T05:00:51.6587861Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:51.6590262Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:00:51.6591801Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:00:51.6597474Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:00:51.6601927Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:00:51.6606555Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:00:51.6607046Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:00:51.6607650Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:00:51.6607966Z         
2025-03-14T05:00:51.6608399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.6608953Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.6609210Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:00:51.6609498Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:00:51.6609783Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.6610043Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:00:51.6610286Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:00:51.6610568Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.6610824Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:00:51.6611062Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:00:51.6611334Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.6611718Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:00:51.6611955Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:00:51.6612193Z         
2025-03-14T05:00:51.6612600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:51.6613422Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:00:51.6614088Z         
2025-03-14T05:00:51.6614625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:51.6615252Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:00:51.6615542Z         
2025-03-14T05:00:51.6615968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:51.6616559Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:00:51.6616860Z         
2025-03-14T05:00:51.6617282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:51.6617831Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:51.6618165Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.6618508Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:00:51.6618789Z         
2025-03-14T05:00:51.6619210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:51.6619732Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:51.6620060Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:51.6620410Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:00:51.6620695Z         
2025-03-14T05:00:51.6621105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:51.6621615Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.6621942Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:00:51.6622254Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:00:51.6622501Z         
2025-03-14T05:00:51.6622916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:51.6623458Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:51.6623764Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:00:51.6624050Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:00:51.6624301Z         
2025-03-14T05:00:51.6624719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:51.6625238Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:51.6625563Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:00:51.6625801Z         
2025-03-14T05:00:51.6626190Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:51.6626697Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:51.6627017Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:00:51.6627250Z         
2025-03-14T05:00:51.6627630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:51.6628135Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:51.6628456Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:00:51.6628688Z         
2025-03-14T05:00:51.6629074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:51.6629634Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:51.6629980Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:00:51.6630211Z         
2025-03-14T05:00:51.6630637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:51.6631163Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:51.6631418Z         
2025-03-14T05:00:51.6631834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:51.6632353Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:51.6632613Z         
2025-03-14T05:00:51.6633048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:51.6633589Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:51.6633915Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:00:51.6634249Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:51.6634596Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:00:51.6634867Z         
2025-03-14T05:00:51.6635307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:51.6635860Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:51.6636195Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:00:51.6636524Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:51.6636865Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:00:51.6637120Z         
2025-03-14T05:00:51.6637540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:51.6638037Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:51.6638366Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:51.6638714Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:00:51.6638962Z         
2025-03-14T05:00:51.6639383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:51.6639889Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:51.6640226Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:51.6640577Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:00:51.6640824Z         
2025-03-14T05:00:51.6641226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:51.6641701Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:51.6641966Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:51.6642200Z         
2025-03-14T05:00:51.6642585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:51.6643034Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:51.6643281Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:51.6643502Z         
2025-03-14T05:00:51.6643879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:51.6644354Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:51.6644642Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:51.6644888Z         
2025-03-14T05:00:51.6645277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:51.6645755Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:51.6646037Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:51.6646280Z         
2025-03-14T05:00:51.6646713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:51.6647308Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:51.6647606Z         
2025-03-14T05:00:51.6648037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:51.6648628Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:00:51.6648921Z         
2025-03-14T05:00:51.6649420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:00:51.6650117Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:00:51.6650555Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:00:51.6650847Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:00:51.6651155Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:00:51.6651551Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:00:51.6651826Z         
2025-03-14T05:00:51.6652228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.6652831Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.6653188Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:00:51.6653437Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:00:51.6653814Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.6654164Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:00:51.6654409Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:00:51.6654777Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.6655162Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:00:51.6655397Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:00:51.6655760Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.6656103Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:00:51.6656337Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:00:51.6656548Z         
2025-03-14T05:00:51.6656975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:00:51.6657603Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:00:51.6657921Z         
2025-03-14T05:00:51.6658370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:00:51.6659059Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:00:51.6659485Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:00:51.6659767Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:00:51.6660063Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:00:51.6660370Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:00:51.6660836Z         
2025-03-14T05:00:51.6661505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:51.6662212Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:00:51.6662591Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:51.6662958Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:00:51.6663300Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:51.6663596Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:51.6663837Z         
2025-03-14T05:00:51.6664281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:51.6664814Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:51.6665050Z         
2025-03-14T05:00:51.6665180Z 
2025-03-14T05:00:51.6665280Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:51.6666098Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:00:51.6666899Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:00:51.6667122Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:00:51.6667433Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:00:51.6667835Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:00:51.6668227Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:00:51.6668644Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:00:51.6668933Z         
2025-03-14T05:00:51.6669315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.6669772Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.6670019Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:00:51.6670254Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:00:51.6670518Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.6670763Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:00:51.6670999Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:00:51.6671271Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.6671518Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:00:51.6671749Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:00:51.6672013Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.6672255Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:00:51.6672483Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:00:51.6672699Z         
2025-03-14T05:00:51.6673066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:51.6673834Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:00:51.6674375Z         
2025-03-14T05:00:51.6674844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:51.6675443Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:00:51.6675713Z         
2025-03-14T05:00:51.6676119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:51.6676650Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:00:51.6676925Z         
2025-03-14T05:00:51.6677324Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:51.6677826Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:51.6678142Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.6678475Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:00:51.6678738Z         
2025-03-14T05:00:51.6679136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:51.6679638Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:51.6679943Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:51.6680269Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:00:51.6680536Z         
2025-03-14T05:00:51.6680920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:51.6681395Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.6681685Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:00:51.6681954Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:00:51.6682190Z         
2025-03-14T05:00:51.6682571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:51.6683069Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:51.6683363Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:00:51.6683637Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:00:51.6683880Z         
2025-03-14T05:00:51.6684259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:51.6684754Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:51.6685069Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:00:51.6685287Z         
2025-03-14T05:00:51.6685667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:51.6686169Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:51.6686486Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:00:51.6686718Z         
2025-03-14T05:00:51.6687111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:51.6687605Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:51.6687941Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:00:51.6688167Z         
2025-03-14T05:00:51.6688565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:51.6689096Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:51.6689438Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:00:51.6689668Z         
2025-03-14T05:00:51.6690087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:51.6690607Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:51.6690861Z         
2025-03-14T05:00:51.6691288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:51.6691900Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:51.6692162Z         
2025-03-14T05:00:51.6692609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:51.6693146Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:51.6693463Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:00:51.6693802Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:51.6695296Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:00:51.6695553Z         
2025-03-14T05:00:51.6695991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:51.6696532Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:51.6696849Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:00:51.6697179Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:51.6697523Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:00:51.6697779Z         
2025-03-14T05:00:51.6698195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:51.6698696Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:51.6699021Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:51.6699362Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:00:51.6699607Z         
2025-03-14T05:00:51.6700016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:51.6700512Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:51.6700861Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:51.6701212Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:00:51.6701466Z         
2025-03-14T05:00:51.6701882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:51.6702372Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:51.6702634Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:51.6702869Z         
2025-03-14T05:00:51.6703266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:51.6703725Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:51.6703982Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:51.6704213Z         
2025-03-14T05:00:51.6704598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:51.6705077Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:51.6705366Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:51.6705613Z         
2025-03-14T05:00:51.6706004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:51.6706492Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:51.6706780Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:51.6707031Z         
2025-03-14T05:00:51.6707481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:51.6708080Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:51.6708403Z         
2025-03-14T05:00:51.6708832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:51.6709408Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:00:51.6709688Z         
2025-03-14T05:00:51.6710131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:00:51.6710808Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:00:51.6711229Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:00:51.6711509Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:00:51.6711806Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:00:51.6712109Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:00:51.6712357Z         
2025-03-14T05:00:51.6712733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.6713284Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.6713634Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:00:51.6713889Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:00:51.6714277Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.6714615Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:00:51.6714852Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:00:51.6715225Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.6715568Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:00:51.6715799Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:00:51.6716153Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.6716485Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:00:51.6716713Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:00:51.6716924Z         
2025-03-14T05:00:51.6717346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:00:51.6717940Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:00:51.6718265Z         
2025-03-14T05:00:51.6718702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:00:51.6719364Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:00:51.6719777Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:00:51.6720061Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:00:51.6720348Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:00:51.6720645Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:00:51.6720892Z         
2025-03-14T05:00:51.6721458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:51.6722158Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:00:51.6722492Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:51.6722815Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:00:51.6723148Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:51.6723431Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:51.6723736Z         
2025-03-14T05:00:51.6724174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:51.6724690Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:51.6724925Z         
2025-03-14T05:00:52.0228585Z 
2025-03-14T05:00:52.0229126Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:52.0229599Z     def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"):
2025-03-14T05:00:52.0230201Z         l_scores_0_ = L_scores_0_
2025-03-14T05:00:52.0230429Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:00:52.0230930Z         
2025-03-14T05:00:52.0231580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:52.0232673Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:00:52.0232999Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:52.0233392Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:00:52.0233757Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:52.0234048Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:52.0234289Z         
2025-03-14T05:00:52.0234735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:52.0235255Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:52.0235493Z         
2025-03-14T05:00:52.0235585Z 
2025-03-14T05:00:52.0237160Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:52.0237638Z     def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"):
2025-03-14T05:00:52.0237997Z         l_scores_0_ = L_scores_0_
2025-03-14T05:00:52.0238222Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:00:52.0238467Z         
2025-03-14T05:00:52.0239137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:52.0239905Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:00:52.0240268Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:52.0240581Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:00:52.0240891Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:52.0242917Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:52.0243448Z         
2025-03-14T05:00:52.0244153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:52.0248630Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:52.0250439Z         
2025-03-14T05:01:03.8425064Z Compilation time (from dynamo_timed): 34.323805659
2025-03-14T05:01:03.8430395Z pass
2025-03-14T05:01:03.8435761Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:01:03.8440945Z TIMING: entire_frame_compile:34.32381 gc:0.0387 _recursive_pre_grad_passes:0.03303 async_compile.wait:3.55815 backend_compile:19.71346 _recursive_joint_graph_passes:0.17275 _recursive_post_grad_passes:0.08319 code_gen:7.13016 inductor_compile:8.49221 total_wall_time:34.32381
2025-03-14T05:01:03.8442381Z STATS: call_* op count: 990 | FakeTensorMode.__torch_dispatch__:22926 | FakeTensor.__torch_dispatch__:1797 | ProxyTorchDispatchMode.__torch_dispatch__:5692 | attempt fast:112 | slow no contiguity match:36 | fast is_contiguous:76
2025-03-14T05:01:03.8443017Z Dynamo produced 61 graphs covering 990 ops with 46 graph breaks (6 unique)
2025-03-14T05:01:09.1570349Z 
2025-03-14T05:01:13.8968068Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:01:13.8972362Z loading model: 0it [00:04, ?it/s]
2025-03-14T05:01:13.8975274Z cpu  eval  detectron2_fcos_r_50_fpn           
2025-03-14T05:01:29.4972384Z WARNING:common:fp64 golden ref were not generated for detectron2_fcos_r_50_fpn. Setting accuracy check to cosine
2025-03-14T05:01:29.5009616Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:01:35.6906520Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:01:41.3157987Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:02:35.2472662Z Compilation time (from dynamo_timed): 49.07104005
2025-03-14T05:02:35.2474917Z pass
2025-03-14T05:02:35.2476873Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:02:35.2478425Z TIMING: entire_frame_compile:49.07104 gc:0.02657 _recursive_pre_grad_passes:0.02439 async_compile.wait:14.98662 backend_compile:37.88347 _recursive_joint_graph_passes:0.27118 _recursive_post_grad_passes:0.17848 code_gen:22.71362 inductor_compile:25.89442 total_wall_time:49.07104
2025-03-14T05:02:35.2479913Z STATS: call_* op count: 944 | FakeTensorMode.__torch_dispatch__:29063 | FakeTensor.__torch_dispatch__:3334 | ProxyTorchDispatchMode.__torch_dispatch__:10972
2025-03-14T05:02:35.2480717Z Dynamo produced 29 graphs covering 944 ops with 22 graph breaks (4 unique)
2025-03-14T05:02:40.5713194Z 
2025-03-14T05:02:53.9977072Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:02:53.9980968Z loading model: 0it [00:13, ?it/s]
2025-03-14T05:02:53.9981348Z Traceback (most recent call last):
2025-03-14T05:02:53.9981881Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 485, in <module>
2025-03-14T05:02:53.9984773Z     torchbench_main()
2025-03-14T05:02:53.9985248Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 481, in torchbench_main
2025-03-14T05:02:53.9985668Z     main(TorchBenchmarkRunner(), original_dir)
2025-03-14T05:02:53.9986004Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3493, in main
2025-03-14T05:02:53.9987401Z     process_entry(0, runner, original_dir, args)
2025-03-14T05:02:53.9987818Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3425, in process_entry
2025-03-14T05:02:53.9988183Z     return run(runner, args, original_dir)
2025-03-14T05:02:53.9988516Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 4070, in run
2025-03-14T05:02:53.9988915Z     assert marked, f"nothing in example_inputs had a dim with {batch_size}"
2025-03-14T05:02:53.9989244Z AssertionError: nothing in example_inputs had a dim with 4
2025-03-14T05:02:54.7857070Z Run failed with return code:  1
2025-03-14T05:02:54.7857381Z Output:  None
2025-03-14T05:02:54.7857552Z Error:  None
2025-03-14T05:02:57.1816894Z 
2025-03-14T05:03:04.6494470Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:03:04.6495731Z loading model: 0it [00:07, ?it/s]
2025-03-14T05:03:04.6496089Z Traceback (most recent call last):
2025-03-14T05:03:04.6496638Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 485, in <module>
2025-03-14T05:03:04.6497092Z     torchbench_main()
2025-03-14T05:03:04.6502218Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 481, in torchbench_main
2025-03-14T05:03:04.6506771Z     main(TorchBenchmarkRunner(), original_dir)
2025-03-14T05:03:04.6510885Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3493, in main
2025-03-14T05:03:04.6515437Z     process_entry(0, runner, original_dir, args)
2025-03-14T05:03:04.6519827Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3425, in process_entry
2025-03-14T05:03:04.6524327Z     return run(runner, args, original_dir)
2025-03-14T05:03:04.6526273Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 4070, in run
2025-03-14T05:03:04.6526688Z     assert marked, f"nothing in example_inputs had a dim with {batch_size}"
2025-03-14T05:03:04.6527013Z AssertionError: nothing in example_inputs had a dim with 4
2025-03-14T05:03:05.3965871Z Run failed with return code:  1
2025-03-14T05:03:05.3970496Z Output:  None
2025-03-14T05:03:05.3970722Z Error:  None
2025-03-14T05:03:07.7611996Z 
2025-03-14T05:03:21.0416358Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:03:21.0416856Z loading model: 0it [00:13, ?it/s]
2025-03-14T05:03:21.0417143Z Traceback (most recent call last):
2025-03-14T05:03:21.0417932Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 485, in <module>
2025-03-14T05:03:21.0418338Z     torchbench_main()
2025-03-14T05:03:21.0418703Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 481, in torchbench_main
2025-03-14T05:03:21.0419216Z     main(TorchBenchmarkRunner(), original_dir)
2025-03-14T05:03:21.0419709Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3493, in main
2025-03-14T05:03:21.0423363Z     process_entry(0, runner, original_dir, args)
2025-03-14T05:03:21.0423953Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3425, in process_entry
2025-03-14T05:03:21.0424446Z     return run(runner, args, original_dir)
2025-03-14T05:03:21.0424883Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 4070, in run
2025-03-14T05:03:21.0425391Z     assert marked, f"nothing in example_inputs had a dim with {batch_size}"
2025-03-14T05:03:21.0425778Z AssertionError: nothing in example_inputs had a dim with 4
2025-03-14T05:03:21.7057954Z Run failed with return code:  1
2025-03-14T05:03:21.7060807Z Output:  None
2025-03-14T05:03:21.7061039Z Error:  None
2025-03-14T05:03:24.0909630Z 
2025-03-14T05:03:31.8452348Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:03:31.8456109Z loading model: 0it [00:07, ?it/s]
2025-03-14T05:03:31.8458091Z Traceback (most recent call last):
2025-03-14T05:03:31.8458663Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 485, in <module>
2025-03-14T05:03:31.8464063Z     torchbench_main()
2025-03-14T05:03:31.8466540Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 481, in torchbench_main
2025-03-14T05:03:31.8467091Z     main(TorchBenchmarkRunner(), original_dir)
2025-03-14T05:03:31.8472792Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3493, in main
2025-03-14T05:03:31.8477477Z     process_entry(0, runner, original_dir, args)
2025-03-14T05:03:31.8479649Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3425, in process_entry
2025-03-14T05:03:31.8480148Z     return run(runner, args, original_dir)
2025-03-14T05:03:31.8484639Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 4070, in run
2025-03-14T05:03:31.8486630Z     assert marked, f"nothing in example_inputs had a dim with {batch_size}"
2025-03-14T05:03:31.8487113Z AssertionError: nothing in example_inputs had a dim with 4
2025-03-14T05:03:32.6264994Z Run failed with return code:  1
2025-03-14T05:03:32.6265638Z Output:  None
2025-03-14T05:03:32.6265934Z Error:  None
2025-03-14T05:03:35.0138441Z 
2025-03-14T05:03:40.7988623Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:03:40.7989027Z loading model: 0it [00:05, ?it/s]
2025-03-14T05:03:40.7989323Z cpu  eval  dlrm                               
2025-03-14T05:03:41.4732262Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:03:41.6617099Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:03:41.8800552Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:03:51.7228156Z Compilation time (from dynamo_timed): 8.504976846
2025-03-14T05:03:51.7232415Z pass
2025-03-14T05:03:51.7234827Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:03:51.7235951Z TIMING: _recursive_pre_grad_passes:0.00364 _recursive_joint_graph_passes:0.10035 _recursive_post_grad_passes:0.01511 async_compile.wait:1.172 code_gen:7.26063 inductor_compile:7.38869 backend_compile:8.16214 entire_frame_compile:8.50498 gc:0.00115 total_wall_time:8.50498
2025-03-14T05:03:51.7240531Z STATS: call_* op count: 36 | FakeTensorMode.__torch_dispatch__:1716 | ProxyTorchDispatchMode.__torch_dispatch__:504 | FakeTensor.__torch_dispatch__:81
2025-03-14T05:03:51.7241226Z Dynamo produced 1 graphs covering 36 ops with 0 graph breaks (0 unique)
2025-03-14T05:03:55.7747733Z 
2025-03-14T05:03:56.8107920Z loading model: 0it [00:00, ?it/s]Downloading https://doctr-static.mindee.com/models?id=v0.7.0/db_resnet50-79bd7d70.pt&src=0 to /var/lib/jenkins/.cache/doctr/models/db_resnet50-79bd7d70.pt
2025-03-14T05:03:57.2411805Z 
2025-03-14T05:03:57.2416573Z 
2025-03-14T05:03:57.3448420Z   0% 0/102021912 [00:00<?, ?it/s]
2025-03-14T05:03:57.3448896Z 
2025-03-14T05:03:57.4413943Z  38% 38564864/102021912 [00:00<00:00, 385646249.42it/s]
2025-03-14T05:03:57.4416566Z 
2025-03-14T05:03:57.4884582Z  80% 81747968/102021912 [00:00<00:00, 412809303.94it/s]
2025-03-14T05:03:57.4887373Z 102022144it [00:00, 413731648.16it/s]                  
2025-03-14T05:03:58.7587899Z Downloading https://doctr-static.mindee.com/models?id=v0.3.1/crnn_vgg16_bn-9762b0b0.pt&src=0 to /var/lib/jenkins/.cache/doctr/models/crnn_vgg16_bn-9762b0b0.pt
2025-03-14T05:03:59.1161570Z 
2025-03-14T05:03:59.1161590Z 
2025-03-14T05:03:59.2166056Z   0% 0/63286381 [00:00<?, ?it/s]
2025-03-14T05:03:59.2166355Z 
2025-03-14T05:03:59.2704676Z  63% 40156160/63286381 [00:00<00:00, 401546665.16it/s]
2025-03-14T05:03:59.2705737Z 63287296it [00:00, 411566765.27it/s]                  
2025-03-14T05:04:02.7065292Z 
2025-03-14T05:04:02.7070232Z loading model: 0it [00:06, ?it/s]
2025-03-14T05:04:02.7086122Z cpu  eval  doctr_det_predictor                
2025-03-14T05:04:18.6092325Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:04:23.7848724Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:04:27.0480202Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:04:57.5953088Z Compilation time (from dynamo_timed): 27.644077799
2025-03-14T05:04:57.5955398Z pass
2025-03-14T05:04:57.5962948Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:04:57.5966421Z TIMING: entire_frame_compile:27.64408 gc:0.00836 _recursive_pre_grad_passes:0.00829 _recursive_joint_graph_passes:0.14853 _recursive_post_grad_passes:0.09902 async_compile.wait:3.13121 code_gen:14.6967 inductor_compile:16.39121 backend_compile:20.16242 total_wall_time:27.64408
2025-03-14T05:04:57.5967941Z STATS: call_* op count: 230 | FakeTensorMode.__torch_dispatch__:16961 | ProxyTorchDispatchMode.__torch_dispatch__:6953 | FakeTensor.__torch_dispatch__:2205
2025-03-14T05:04:57.5973753Z Dynamo produced 4 graphs covering 230 ops with 5 graph breaks (2 unique)
2025-03-14T05:05:02.4289028Z 
2025-03-14T05:05:05.1491977Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:05:05.1492415Z loading model: 0it [00:02, ?it/s]
2025-03-14T05:05:05.1498327Z cpu  eval  doctr_reco_predictor               
2025-03-14T05:05:05.3714370Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:05:05.4328621Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:05:05.5524298Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:05:20.3601677Z W0314 05:05:20.358378 64155 site-packages/torch/fx/experimental/symbolic_shapes.py:6679] [2/0] failed during evaluate_expr(u0, hint=None, size_oblivious=False, forcing_spec=False
2025-03-14T05:05:20.3602761Z E0314 05:05:20.358705 64155 site-packages/torch/fx/experimental/recording.py:299] [2/0] failed while running evaluate_expr(*(u0, None, u0, False), **{})
2025-03-14T05:05:20.4168555Z W0314 05:05:20.415436 64155 site-packages/torch/fx/experimental/symbolic_shapes.py:6679] [3/0] failed during evaluate_expr(u0, hint=None, size_oblivious=False, forcing_spec=False
2025-03-14T05:05:20.4169504Z E0314 05:05:20.415721 64155 site-packages/torch/fx/experimental/recording.py:299] [3/0] failed while running evaluate_expr(*(u0, None, u0, False), **{})
2025-03-14T05:05:21.9784041Z Compilation time (from dynamo_timed): 15.332880932
2025-03-14T05:05:21.9784595Z pass
2025-03-14T05:05:21.9794615Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:05:21.9796655Z TIMING: entire_frame_compile:15.33288 gc:0.00419 _recursive_pre_grad_passes:0.00454 _recursive_joint_graph_passes:0.10206 _recursive_post_grad_passes:0.03468 async_compile.wait:2.56162 code_gen:9.53736 inductor_compile:10.45865 backend_compile:13.01524 total_wall_time:15.33288
2025-03-14T05:05:21.9798161Z STATS: call_* op count: 56 | FakeTensorMode.__torch_dispatch__:6705 | attempt fast:417 | fast is_contiguous:417 | ProxyTorchDispatchMode.__torch_dispatch__:1316 | FakeTensor.__torch_dispatch__:447
2025-03-14T05:05:21.9798815Z Dynamo produced 2 graphs covering 56 ops with 4 graph breaks (3 unique)
2025-03-14T05:05:26.4956005Z 
2025-03-14T05:05:27.4966484Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:05:27.4966830Z loading model: 0it [00:01, ?it/s]
2025-03-14T05:05:27.4967068Z cpu  eval  drq                                
2025-03-14T05:05:27.5311985Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:05:27.5394912Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:05:27.5597278Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:05:37.0316711Z Compilation time (from dynamo_timed): 8.504092689
2025-03-14T05:05:37.0317116Z pass
2025-03-14T05:05:37.0317426Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:05:37.0318226Z TIMING: _recursive_pre_grad_passes:0.00414 _recursive_joint_graph_passes:0.09805 _recursive_post_grad_passes:0.01577 async_compile.wait:1.45109 code_gen:7.13458 inductor_compile:7.23238 backend_compile:7.99462 entire_frame_compile:8.50409 gc:0.00172 total_wall_time:8.50409
2025-03-14T05:05:37.0319124Z STATS: call_* op count: 58 | FakeTensor.__torch_dispatch__:72 | FakeTensorMode.__torch_dispatch__:1118 | ProxyTorchDispatchMode.__torch_dispatch__:290
2025-03-14T05:05:37.0319673Z Dynamo produced 2 graphs covering 58 ops with 0 graph breaks (0 unique)
2025-03-14T05:05:41.1750947Z 
2025-03-14T05:05:42.5479650Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:05:42.5486134Z loading model: 0it [00:01, ?it/s]
2025-03-14T05:05:42.5489706Z cpu  eval  fastNLP_Bert                       
2025-03-14T05:05:43.7916564Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:05:44.3603248Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:05:44.9494815Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:05:46.0130848Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] Graph break from `Tensor.item()`, consider setting:
2025-03-14T05:05:46.0136184Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     torch._dynamo.config.capture_scalar_outputs = True
2025-03-14T05:05:46.0138069Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] or:
2025-03-14T05:05:46.0138695Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
2025-03-14T05:05:46.0144342Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] to include these operations in the captured graph.
2025-03-14T05:05:46.0145003Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] 
2025-03-14T05:05:46.0145560Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] Graph break: from user code at:
2025-03-14T05:05:46.0146226Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 459, in forward_pass
2025-03-14T05:05:46.0147145Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     return mod(*inputs)
2025-03-14T05:05:46.0147803Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/fastNLP/models/bert.py", line 265, in forward
2025-03-14T05:05:46.0148525Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     sequence_output = self.bert(words)
2025-03-14T05:05:46.0152336Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/fastNLP/embeddings/bert_embedding.py", line 137, in forward
2025-03-14T05:05:46.0153289Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     outputs = self.model(words)
2025-03-14T05:05:46.0154173Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/fastNLP/embeddings/bert_embedding.py", line 445, in forward
2025-03-14T05:05:46.0155694Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     max_word_piece_length = batch_word_pieces_length.sum(dim=-1).max().item()  # 表示word piece的长度(包括padding)
2025-03-14T05:05:46.0156368Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] 
2025-03-14T05:05:46.0156824Z W0314 05:05:46.011378 66031 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] 
2025-03-14T05:06:04.4255377Z Compilation time (from dynamo_timed): 17.311447712
2025-03-14T05:06:04.4260414Z pass
2025-03-14T05:06:04.4264858Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:06:04.4266968Z TIMING: entire_frame_compile:17.31145 gc:0.00539 _recursive_pre_grad_passes:0.00837 _recursive_joint_graph_passes:0.39129 _recursive_post_grad_passes:0.05136 async_compile.wait:3.67323 code_gen:10.57061 inductor_compile:11.74799 backend_compile:14.67503 total_wall_time:17.31145
2025-03-14T05:06:04.4268022Z STATS: call_* op count: 440 | FakeTensorMode.__torch_dispatch__:14852 | ProxyTorchDispatchMode.__torch_dispatch__:6105 | FakeTensor.__torch_dispatch__:1148
2025-03-14T05:06:04.4273803Z Dynamo produced 4 graphs covering 440 ops with 4 graph breaks (1 unique)
2025-03-14T05:06:08.7986030Z 
2025-03-14T05:06:09.1753278Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:06:09.1756031Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:06:09.1756413Z cpu  eval  functorch_dp_cifar10               
2025-03-14T05:06:09.2160906Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:06:09.2372503Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:06:09.2542572Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:06:25.9501511Z Compilation time (from dynamo_timed): 15.73155022
2025-03-14T05:06:25.9502093Z pass
2025-03-14T05:06:25.9502483Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:06:25.9503441Z TIMING: _recursive_pre_grad_passes:0.00393 _recursive_joint_graph_passes:0.14679 _recursive_post_grad_passes:0.03263 async_compile.wait:1.51084 code_gen:9.02064 inductor_compile:10.77643 backend_compile:14.20613 entire_frame_compile:15.73155 gc:0.00092 total_wall_time:15.73155
2025-03-14T05:06:25.9509090Z STATS: call_* op count: 69 | FakeTensorMode.__torch_dispatch__:6709 | attempt fast:453 | fast is_contiguous:453 | ProxyTorchDispatchMode.__torch_dispatch__:1158 | FakeTensor.__torch_dispatch__:545
2025-03-14T05:06:25.9512519Z Dynamo produced 1 graphs covering 69 ops with 0 graph breaks (0 unique)
2025-03-14T05:06:30.3440482Z 
2025-03-14T05:06:30.5765072Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:06:30.5765490Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:06:30.5766287Z cpu  eval  functorch_maml_omniglot            
2025-03-14T05:06:30.5920456Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:06:30.5962617Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:06:30.5992947Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:06:40.2600649Z Compilation time (from dynamo_timed): 8.56806734
2025-03-14T05:06:40.2603091Z pass
2025-03-14T05:06:40.2603687Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:06:40.2607277Z TIMING: _recursive_pre_grad_passes:0.00298 _recursive_joint_graph_passes:0.09523 _recursive_post_grad_passes:0.01529 async_compile.wait:1.32404 code_gen:7.44285 inductor_compile:7.56647 backend_compile:8.32021 entire_frame_compile:8.56807 gc:0.00072 total_wall_time:8.56807
2025-03-14T05:06:40.2608448Z STATS: call_* op count: 14 | FakeTensor.__torch_dispatch__:78 | FakeTensorMode.__torch_dispatch__:1086 | ProxyTorchDispatchMode.__torch_dispatch__:289
2025-03-14T05:06:40.2608969Z Dynamo produced 1 graphs covering 14 ops with 0 graph breaks (0 unique)
2025-03-14T05:06:44.2498588Z 
2025-03-14T05:06:46.3812892Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:06:46.3813936Z loading model: 0it [00:02, ?it/s]
2025-03-14T05:06:46.3818812Z cpu  eval  hf_Albert                          
2025-03-14T05:06:48.3198721Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:06:49.3573229Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:06:49.9581550Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:07:17.6514143Z Compilation time (from dynamo_timed): 25.111979198
2025-03-14T05:07:17.6514448Z pass
2025-03-14T05:07:17.6521507Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:07:17.6522354Z TIMING: _recursive_pre_grad_passes:0.00664 _recursive_joint_graph_passes:0.64861 _recursive_post_grad_passes:0.06387 async_compile.wait:1.38502 code_gen:8.55794 inductor_compile:11.13066 backend_compile:21.3075 entire_frame_compile:25.11198 gc:0.0004 total_wall_time:25.11198
2025-03-14T05:07:17.6523399Z STATS: call_* op count: 437 | FakeTensorMode.__torch_dispatch__:21387 | attempt fast:1638 | fast is_contiguous:1638 | ProxyTorchDispatchMode.__torch_dispatch__:5346 | FakeTensor.__torch_dispatch__:1230
2025-03-14T05:07:17.6524300Z Dynamo produced 1 graphs covering 437 ops with 0 graph breaks (0 unique)
2025-03-14T05:07:22.6839999Z 
2025-03-14T05:07:26.1594789Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:07:26.1595794Z loading model: 0it [00:03, ?it/s]
2025-03-14T05:07:26.1612176Z cpu  eval  hf_Bart                            
2025-03-14T05:07:27.9411455Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:07:28.7499580Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:07:29.5626181Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:08:00.1427044Z Compilation time (from dynamo_timed): 28.354229841
2025-03-14T05:08:00.1818450Z pass
2025-03-14T05:08:00.1826509Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:08:00.1831720Z TIMING: _recursive_pre_grad_passes:0.00887 _recursive_joint_graph_passes:0.51436 _recursive_post_grad_passes:0.09525 async_compile.wait:1.22259 code_gen:8.91979 inductor_compile:12.70816 backend_compile:23.21043 entire_frame_compile:28.35423 gc:0.00159 total_wall_time:28.35423
2025-03-14T05:08:00.1838040Z STATS: call_* op count: 451 | FakeTensorMode.__torch_dispatch__:25721 | attempt fast:1741 | fast is_contiguous:1740 | slow no contiguity match:1 | ProxyTorchDispatchMode.__torch_dispatch__:6179 | FakeTensor.__torch_dispatch__:1508
2025-03-14T05:08:00.1838793Z Dynamo produced 1 graphs covering 451 ops with 0 graph breaks (0 unique)
2025-03-14T05:08:05.1955645Z 
2025-03-14T05:08:07.7480286Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:08:07.7483812Z loading model: 0it [00:02, ?it/s]
2025-03-14T05:08:07.7489243Z cpu  eval  hf_Bert                            
2025-03-14T05:08:09.9631775Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:08:10.6025633Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:08:11.2261389Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:08:39.6250594Z Compilation time (from dynamo_timed): 26.198511781
2025-03-14T05:08:39.6385819Z pass
2025-03-14T05:08:39.6386285Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:08:39.6392449Z TIMING: _recursive_pre_grad_passes:0.00677 _recursive_joint_graph_passes:0.63257 _recursive_post_grad_passes:0.05706 async_compile.wait:1.35069 code_gen:7.93476 inductor_compile:10.86644 backend_compile:21.21995 entire_frame_compile:26.19851 gc:0.00049 total_wall_time:26.19851
2025-03-14T05:08:39.6393522Z STATS: call_* op count: 367 | FakeTensorMode.__torch_dispatch__:22597 | attempt fast:1526 | fast is_contiguous:1526 | ProxyTorchDispatchMode.__torch_dispatch__:5845 | FakeTensor.__torch_dispatch__:1134
2025-03-14T05:08:39.6394136Z Dynamo produced 1 graphs covering 367 ops with 0 graph breaks (0 unique)
2025-03-14T05:08:44.8329244Z 
2025-03-14T05:08:51.1380910Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:08:51.1383773Z loading model: 0it [00:06, ?it/s]
2025-03-14T05:08:51.1402215Z cpu  eval  hf_Bert_large                      
2025-03-14T05:08:57.9514193Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:08:59.8861315Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:09:01.6225300Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:09:48.3777567Z Compilation time (from dynamo_timed): 43.784545639
2025-03-14T05:09:48.3936026Z pass
2025-03-14T05:09:48.3942346Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:09:48.3944523Z TIMING: _recursive_pre_grad_passes:0.01171 _recursive_joint_graph_passes:1.1497 _recursive_post_grad_passes:0.10262 async_compile.wait:1.35618 code_gen:8.9413 inductor_compile:14.32833 backend_compile:34.17888 entire_frame_compile:43.78455 gc:0.00043 total_wall_time:43.78455
2025-03-14T05:09:48.3945590Z STATS: call_* op count: 715 | FakeTensorMode.__torch_dispatch__:44101 | attempt fast:2966 | fast is_contiguous:2966 | ProxyTorchDispatchMode.__torch_dispatch__:11437 | FakeTensor.__torch_dispatch__:2202
2025-03-14T05:09:48.3949835Z Dynamo produced 1 graphs covering 715 ops with 0 graph breaks (0 unique)
2025-03-14T05:09:54.1884347Z 
2025-03-14T05:09:56.3191447Z loading model: 0it [00:00, ?it/s]Input ids are automatically padded from 819 to 832 to be a multiple of `config.block_size`: 64
2025-03-14T05:09:57.6245114Z 
2025-03-14T05:09:57.6249596Z loading model: 0it [00:03, ?it/s]
2025-03-14T05:09:57.6251717Z cpu  eval  hf_BigBird                         
2025-03-14T05:10:01.3746759Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:10:03.2414745Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:10:04.6523760Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:10:35.2724404Z W0314 05:10:35.271438 71589 site-packages/torch/_dynamo/convert_frame.py:964] [10/8] torch._dynamo hit config.recompile_limit (8)
2025-03-14T05:10:35.2726430Z W0314 05:10:35.271438 71589 site-packages/torch/_dynamo/convert_frame.py:964] [10/8]    function: 'bigbird_block_sparse_attention' (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/big_bird/modeling_big_bird.py:516)
2025-03-14T05:10:35.2728488Z W0314 05:10:35.271438 71589 site-packages/torch/_dynamo/convert_frame.py:964] [10/8]    last reason: 10/7: seed == 7  # np.random.seed(seed)  # transformers/models/big_bird/modeling_big_bird.py:569 in bigbird_block_sparse_attention (random.py:42 in manual_seed)
2025-03-14T05:10:35.2730002Z W0314 05:10:35.271438 71589 site-packages/torch/_dynamo/convert_frame.py:964] [10/8] To log all recompilation reasons, use TORCH_LOGS="recompiles".
2025-03-14T05:10:35.2731435Z W0314 05:10:35.271438 71589 site-packages/torch/_dynamo/convert_frame.py:964] [10/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html.
2025-03-14T05:10:39.4276628Z Compilation time (from dynamo_timed): 30.820228188
2025-03-14T05:10:39.4854478Z pass
2025-03-14T05:10:39.4857558Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:10:39.4859156Z TIMING: entire_frame_compile:30.82023 gc:0.01741 _recursive_pre_grad_passes:0.01529 _recursive_joint_graph_passes:0.24045 _recursive_post_grad_passes:0.07165 async_compile.wait:11.2896 code_gen:23.30171 inductor_compile:24.72136 backend_compile:27.2459 total_wall_time:30.82023
2025-03-14T05:10:39.4860409Z STATS: call_* op count: 324 | FakeTensorMode.__torch_dispatch__:10568 | ProxyTorchDispatchMode.__torch_dispatch__:3125 | FakeTensor.__torch_dispatch__:719 | attempt fast:99 | slow both tensors nontrivially broadcast:5 | fast is_contiguous:94
2025-03-14T05:10:39.4861273Z Dynamo produced 23 graphs covering 324 ops with 18 graph breaks (2 unique)
2025-03-14T05:10:44.1826932Z 
2025-03-14T05:10:46.4012016Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:10:46.4012469Z loading model: 0it [00:02, ?it/s]
2025-03-14T05:10:46.4034255Z cpu  eval  hf_DistilBert                      
2025-03-14T05:10:47.6850245Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:10:48.1651524Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:10:48.6912794Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:11:09.1762108Z Compilation time (from dynamo_timed): 18.429763417
2025-03-14T05:11:09.2004523Z pass
2025-03-14T05:11:09.2009394Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:11:09.2011734Z TIMING: _recursive_pre_grad_passes:0.00514 _recursive_joint_graph_passes:0.32734 _recursive_post_grad_passes:0.15306 async_compile.wait:1.16519 code_gen:8.18502 inductor_compile:10.09179 backend_compile:15.91691 entire_frame_compile:18.42976 gc:0.00072 total_wall_time:18.42976
2025-03-14T05:11:09.2012892Z STATS: call_* op count: 204 | FakeTensorMode.__torch_dispatch__:12601 | attempt fast:802 | fast is_contiguous:778 | slow no contiguity match:24 | ProxyTorchDispatchMode.__torch_dispatch__:3028 | FakeTensor.__torch_dispatch__:764
2025-03-14T05:11:09.2013533Z Dynamo produced 1 graphs covering 204 ops with 0 graph breaks (0 unique)
2025-03-14T05:11:13.8153376Z 
2025-03-14T05:11:16.2416974Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:11:16.2417419Z loading model: 0it [00:02, ?it/s]
2025-03-14T05:11:16.2432548Z cpu  eval  hf_GPT2                            
2025-03-14T05:11:16.8967080Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:11:17.1609476Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:11:17.4111042Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:11:43.8088737Z Compilation time (from dynamo_timed): 24.922633769
2025-03-14T05:11:43.8256352Z pass
2025-03-14T05:11:43.8261467Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:11:43.8262778Z TIMING: _recursive_pre_grad_passes:0.00972 _recursive_joint_graph_passes:0.58601 _recursive_post_grad_passes:0.0601 async_compile.wait:1.28185 code_gen:8.11613 inductor_compile:10.93805 backend_compile:20.63157 entire_frame_compile:24.92263 gc:0.00033 total_wall_time:24.92263
2025-03-14T05:11:43.8270125Z STATS: call_* op count: 622 | FakeTensorMode.__torch_dispatch__:20623 | attempt fast:1332 | fast is_contiguous:1332 | ProxyTorchDispatchMode.__torch_dispatch__:5501 | FakeTensor.__torch_dispatch__:1132
2025-03-14T05:11:43.8270819Z Dynamo produced 1 graphs covering 622 ops with 0 graph breaks (0 unique)
2025-03-14T05:11:49.0080412Z 
2025-03-14T05:12:07.0592393Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:12:07.0597231Z loading model: 0it [00:18, ?it/s]
2025-03-14T05:12:07.0624273Z cpu  eval  hf_GPT2_large                      
2025-03-14T05:12:07.2532809Z Compilation time (from dynamo_timed): 0
2025-03-14T05:12:07.2533209Z pass_due_to_skip
2025-03-14T05:12:07.2533548Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:12:07.2533962Z TIMING: total_wall_time:0
2025-03-14T05:12:07.2534179Z STATS: call_* op count: 0
2025-03-14T05:12:07.2534519Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique)
2025-03-14T05:12:10.7098342Z 
2025-03-14T05:12:14.2568043Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:12:14.2569260Z loading model: 0it [00:03, ?it/s]
2025-03-14T05:12:14.2581446Z cpu  eval  hf_Longformer                      
2025-03-14T05:12:19.3375887Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:12:20.4126213Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:12:21.6354324Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:12:22.9109736Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] Graph break from `Tensor.item()`, consider setting:
2025-03-14T05:12:22.9110607Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     torch._dynamo.config.capture_scalar_outputs = True
2025-03-14T05:12:22.9116712Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] or:
2025-03-14T05:12:22.9118717Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
2025-03-14T05:12:22.9119420Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] to include these operations in the captured graph.
2025-03-14T05:12:22.9124601Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] 
2025-03-14T05:12:22.9125179Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] Graph break: from user code at:
2025-03-14T05:12:22.9125880Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 457, in forward_pass
2025-03-14T05:12:22.9126526Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     return mod(**inputs)
2025-03-14T05:12:22.9127279Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/longformer/modeling_longformer.py", line 1835, in forward
2025-03-14T05:12:22.9128009Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     outputs = self.longformer(
2025-03-14T05:12:22.9128947Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/longformer/modeling_longformer.py", line 1738, in forward
2025-03-14T05:12:22.9129694Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     encoder_outputs = self.encoder(
2025-03-14T05:12:22.9130488Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/longformer/modeling_longformer.py", line 1291, in forward
2025-03-14T05:12:22.9131391Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     is_global_attn = is_index_global_attn.flatten().any().item()
2025-03-14T05:12:22.9131900Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] 
2025-03-14T05:12:22.9132290Z W0314 05:12:22.909581 74695 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] 
2025-03-14T05:15:14.0778030Z Compilation time (from dynamo_timed): 169.589111039
2025-03-14T05:15:14.1144564Z pass
2025-03-14T05:15:14.1147648Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:15:14.1148528Z TIMING: entire_frame_compile:169.58911 gc:0.00635 _recursive_pre_grad_passes:0.0207 _recursive_joint_graph_passes:2.18712 _recursive_post_grad_passes:4.06412 async_compile.wait:5.88608 code_gen:73.99666 inductor_compile:95.39531 backend_compile:155.2021 total_wall_time:169.58911
2025-03-14T05:15:14.1152021Z STATS: call_* op count: 1849 | FakeTensorMode.__torch_dispatch__:89589 | attempt fast:1460 | fast is_contiguous:1376 | ProxyTorchDispatchMode.__torch_dispatch__:21836 | FakeTensor.__torch_dispatch__:5923 | slow both tensors nontrivially broadcast:48 | slow no contiguity match:36
2025-03-14T05:15:14.1156959Z Dynamo produced 4 graphs covering 1849 ops with 4 graph breaks (1 unique)
2025-03-14T05:15:40.2429783Z 
2025-03-14T05:15:41.5941037Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:15:41.5941469Z loading model: 0it [00:01, ?it/s]
2025-03-14T05:15:41.5946973Z cpu  eval  hf_Reformer                        
2025-03-14T05:15:44.0681901Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:15:44.8346336Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:15:45.4092364Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:15:46.3528453Z 
2025-03-14T05:15:46.3529173Z class GraphModule(torch.nn.Module):
2025-03-14T05:15:46.3529634Z     def forward(self, s0: "Sym(s0)", L_inputs_input_ids_: "i64[s0, 2048][2048, 1]cpu"):
2025-03-14T05:15:46.3536293Z         l_inputs_input_ids_ = L_inputs_input_ids_
2025-03-14T05:15:46.3540595Z         
2025-03-14T05:15:46.3545075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py:2035 in forward, code: input_shape = input_ids.size()  # noqa: F841
2025-03-14T05:15:46.3546395Z         size = l_inputs_input_ids_.size();  l_inputs_input_ids_ = None
2025-03-14T05:15:46.3546703Z         getitem: "Sym(s0)" = size[0];  getitem = None
2025-03-14T05:15:46.3546990Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:15:46.3547222Z         
2025-03-14T05:15:46.3550854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py:91 in _get_least_common_mult_chunk_len, code: return np.lcm(config.lsh_attn_chunk_length, config.local_attn_chunk_length)
2025-03-14T05:15:46.3551644Z         least_common_mult_chunk_length: "i64[][]cpu" = torch__dynamo_utils_wrapped_lcm(64, 64)
2025-03-14T05:15:46.3551945Z         
2025-03-14T05:15:46.3552438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py:2061 in forward, code: input_shape[-1] % least_common_mult_chunk_length != 0
2025-03-14T05:15:46.3553473Z         wrapped_mod: "i64[][]cpu" = torch__dynamo_utils_wrapped_mod(2048, least_common_mult_chunk_length);  least_common_mult_chunk_length = None
2025-03-14T05:15:46.3553998Z         wrapped_ne: "b8[][]cpu" = torch__dynamo_utils_wrapped_ne(wrapped_mod, 0);  wrapped_mod = wrapped_ne = None
2025-03-14T05:15:46.3554323Z         
2025-03-14T05:15:46.3554412Z 
2025-03-14T05:15:46.3554515Z class GraphModule(torch.nn.Module):
2025-03-14T05:15:46.3554874Z     def forward(self, s0: "Sym(s0)", L_inputs_input_ids_: "i64[s0, 2048][2048, 1]cpu"):
2025-03-14T05:15:46.3555189Z         l_inputs_input_ids_ = L_inputs_input_ids_
2025-03-14T05:15:46.3555418Z         
2025-03-14T05:15:46.3555890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py:2035 in forward, code: input_shape = input_ids.size()  # noqa: F841
2025-03-14T05:15:46.3556452Z         size = l_inputs_input_ids_.size();  l_inputs_input_ids_ = None
2025-03-14T05:15:46.3556728Z         getitem: "Sym(s0)" = size[0];  getitem = None
2025-03-14T05:15:46.3556976Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:15:46.3557189Z         
2025-03-14T05:15:46.3558064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py:91 in _get_least_common_mult_chunk_len, code: return np.lcm(config.lsh_attn_chunk_length, config.local_attn_chunk_length)
2025-03-14T05:15:46.3558757Z         least_common_mult_chunk_length: "i64[][]cpu" = torch__dynamo_utils_wrapped_lcm(64, 64)
2025-03-14T05:15:46.3559042Z         
2025-03-14T05:15:46.3559508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py:2061 in forward, code: input_shape[-1] % least_common_mult_chunk_length != 0
2025-03-14T05:15:46.3560187Z         wrapped_mod: "i64[][]cpu" = torch__dynamo_utils_wrapped_mod(2048, least_common_mult_chunk_length);  least_common_mult_chunk_length = None
2025-03-14T05:15:46.3560918Z         wrapped_ne: "b8[][]cpu" = torch__dynamo_utils_wrapped_ne(wrapped_mod, 0);  wrapped_mod = wrapped_ne = None
2025-03-14T05:15:46.3561234Z         
2025-03-14T05:15:46.4030497Z 
2025-03-14T05:15:46.4031149Z class GraphModule(torch.nn.Module):
2025-03-14T05:15:46.4031656Z     def forward(self, s0: "Sym(s0)", L_input_ids_: "i64[s0, 2048][2048, 1]cpu"):
2025-03-14T05:15:46.4035525Z         l_input_ids_ = L_input_ids_
2025-03-14T05:15:46.4040988Z         
2025-03-14T05:15:46.4043694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py:2035 in forward, code: input_shape = input_ids.size()  # noqa: F841
2025-03-14T05:15:46.4044651Z         size = l_input_ids_.size();  l_input_ids_ = None
2025-03-14T05:15:46.4044919Z         getitem: "Sym(s0)" = size[0];  getitem = None
2025-03-14T05:15:46.4045176Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:15:46.4045394Z         
2025-03-14T05:15:46.4045985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py:91 in _get_least_common_mult_chunk_len, code: return np.lcm(config.lsh_attn_chunk_length, config.local_attn_chunk_length)
2025-03-14T05:15:46.4046716Z         least_common_mult_chunk_length: "i64[][]cpu" = torch__dynamo_utils_wrapped_lcm(64, 64)
2025-03-14T05:15:46.4047009Z         
2025-03-14T05:15:46.4047678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py:2061 in forward, code: input_shape[-1] % least_common_mult_chunk_length != 0
2025-03-14T05:15:46.4048405Z         wrapped_mod: "i64[][]cpu" = torch__dynamo_utils_wrapped_mod(2048, least_common_mult_chunk_length);  least_common_mult_chunk_length = None
2025-03-14T05:15:46.4048914Z         wrapped_ne: "b8[][]cpu" = torch__dynamo_utils_wrapped_ne(wrapped_mod, 0);  wrapped_mod = wrapped_ne = None
2025-03-14T05:15:46.4049237Z         
2025-03-14T05:15:53.4500146Z W0314 05:15:53.448945 75757 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] Graph break from `Tensor.item()`, consider setting:
2025-03-14T05:15:53.4501582Z W0314 05:15:53.448945 75757 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]     torch._dynamo.config.capture_scalar_outputs = True
2025-03-14T05:15:53.4502449Z W0314 05:15:53.448945 75757 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] or:
2025-03-14T05:15:53.4503333Z W0314 05:15:53.448945 75757 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
2025-03-14T05:15:53.4504314Z W0314 05:15:53.448945 75757 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] to include these operations in the captured graph.
2025-03-14T05:15:53.4505118Z W0314 05:15:53.448945 75757 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] 
2025-03-14T05:15:53.4505830Z W0314 05:15:53.448945 75757 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] Graph break: from user code at:
2025-03-14T05:15:53.4507184Z W0314 05:15:53.448945 75757 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py", line 2094, in torch_dynamo_resume_in_forward_at_2066
2025-03-14T05:15:53.4508759Z W0314 05:15:53.448945 75757 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]     embedding_output = self.embeddings(
2025-03-14T05:15:53.4510016Z W0314 05:15:53.448945 75757 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py", line 266, in forward
2025-03-14T05:15:53.4511346Z W0314 05:15:53.448945 75757 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]     position_embeddings = self.position_embeddings(position_ids)
2025-03-14T05:15:53.4512642Z W0314 05:15:53.448945 75757 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py", line 190, in forward
2025-03-14T05:15:53.4513904Z W0314 05:15:53.448945 75757 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]     max_position_id = position_ids.max().item()
2025-03-14T05:15:53.4514702Z W0314 05:15:53.448945 75757 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] 
2025-03-14T05:15:53.4515322Z W0314 05:15:53.448945 75757 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] 
2025-03-14T05:16:08.4223102Z Compilation time (from dynamo_timed): 21.547181993
2025-03-14T05:16:08.4224724Z pass
2025-03-14T05:16:08.4230162Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:16:08.4232787Z TIMING: entire_frame_compile:21.54718 gc:0.00805 _recursive_pre_grad_passes:0.01242 _recursive_joint_graph_passes:0.27862 _recursive_post_grad_passes:0.05406 async_compile.wait:7.35515 code_gen:15.65468 inductor_compile:17.38905 backend_compile:19.21062 total_wall_time:21.54718
2025-03-14T05:16:08.4234094Z STATS: call_* op count: 483 | FakeTensorMode.__torch_dispatch__:12178 | ProxyTorchDispatchMode.__torch_dispatch__:4614 | FakeTensor.__torch_dispatch__:1103 | attempt fast:1 | fast is_contiguous:1
2025-03-14T05:16:08.4238453Z Dynamo produced 7 graphs covering 483 ops with 5 graph breaks (2 unique)
2025-03-14T05:16:12.9630951Z 
2025-03-14T05:16:19.0489405Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:16:19.0489707Z loading model: 0it [00:06, ?it/s]
2025-03-14T05:16:19.0505999Z cpu  eval  hf_Roberta_base                    
2025-03-14T05:16:22.3506162Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:16:23.9382349Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:16:25.5465978Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:16:56.6332637Z Compilation time (from dynamo_timed): 26.873763171
2025-03-14T05:16:56.8075133Z pass
2025-03-14T05:16:56.8078204Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:16:56.8079453Z TIMING: _recursive_pre_grad_passes:0.00674 _recursive_joint_graph_passes:0.62261 _recursive_post_grad_passes:0.05914 async_compile.wait:1.24677 code_gen:8.61026 inductor_compile:11.58309 backend_compile:21.87929 entire_frame_compile:26.87376 gc:0.00015 total_wall_time:26.87376
2025-03-14T05:16:56.8080655Z STATS: call_* op count: 374 | FakeTensorMode.__torch_dispatch__:22817 | attempt fast:1538 | fast is_contiguous:1538 | ProxyTorchDispatchMode.__torch_dispatch__:5884 | FakeTensor.__torch_dispatch__:1152
2025-03-14T05:16:56.8081300Z Dynamo produced 1 graphs covering 374 ops with 0 graph breaks (0 unique)
2025-03-14T05:17:01.9620439Z 
2025-03-14T05:17:03.9057461Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:17:03.9058579Z loading model: 0it [00:01, ?it/s]
2025-03-14T05:17:03.9070085Z cpu  eval  hf_T5                              
2025-03-14T05:17:05.0005545Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:17:05.8001990Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:17:06.5095037Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:17:42.2333711Z Compilation time (from dynamo_timed): 33.659913439
2025-03-14T05:17:42.2689596Z pass
2025-03-14T05:17:42.2698371Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:17:42.2700528Z TIMING: _recursive_pre_grad_passes:0.01041 _recursive_joint_graph_passes:0.70964 _recursive_post_grad_passes:0.4119 async_compile.wait:1.59302 code_gen:10.25037 inductor_compile:14.84773 backend_compile:28.46351 entire_frame_compile:33.65991 gc:0.00067 total_wall_time:33.65991
2025-03-14T05:17:42.2701946Z STATS: call_* op count: 795 | FakeTensorMode.__torch_dispatch__:26902 | attempt fast:811 | fast is_contiguous:764 | slow both tensors nontrivially broadcast:11 | slow no contiguity match:36 | ProxyTorchDispatchMode.__torch_dispatch__:7574 | FakeTensor.__torch_dispatch__:1626
2025-03-14T05:17:42.2703979Z Dynamo produced 1 graphs covering 795 ops with 0 graph breaks (0 unique)
2025-03-14T05:17:47.8911133Z 
2025-03-14T05:18:01.2115820Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:18:01.2116114Z loading model: 0it [00:13, ?it/s]
2025-03-14T05:18:01.2140052Z cpu  eval  hf_T5_base                         
2025-03-14T05:18:32.6211374Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:18:44.1598771Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:19:02.8495367Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:20:16.2921249Z Compilation time (from dynamo_timed): 58.96305212
2025-03-14T05:20:16.2926704Z pass
2025-03-14T05:20:16.2928258Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:20:16.2929236Z TIMING: _recursive_pre_grad_passes:0.01641 _recursive_joint_graph_passes:1.30537 _recursive_post_grad_passes:0.81331 async_compile.wait:1.34694 code_gen:14.48044 inductor_compile:23.13386 backend_compile:49.06952 entire_frame_compile:58.96305 gc:0.00062 total_wall_time:58.96305
2025-03-14T05:20:16.2935329Z STATS: call_* op count: 1491 | FakeTensorMode.__torch_dispatch__:51736 | attempt fast:1549 | fast is_contiguous:1466 | slow both tensors nontrivially broadcast:11 | slow no contiguity match:72 | ProxyTorchDispatchMode.__torch_dispatch__:14510 | FakeTensor.__torch_dispatch__:3108
2025-03-14T05:20:16.2936134Z Dynamo produced 1 graphs covering 1491 ops with 0 graph breaks (0 unique)
2025-03-14T05:20:23.6066471Z 
2025-03-14T05:20:34.4011837Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:20:34.4017889Z loading model: 0it [00:10, ?it/s]
2025-03-14T05:20:34.4051857Z cpu  eval  hf_T5_large                        
2025-03-14T05:20:34.6028167Z Compilation time (from dynamo_timed): 0
2025-03-14T05:20:34.6033089Z pass_due_to_skip
2025-03-14T05:20:34.6037391Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:20:34.6041441Z TIMING: total_wall_time:0
2025-03-14T05:20:34.6042410Z STATS: call_* op count: 0
2025-03-14T05:20:34.6042880Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique)
2025-03-14T05:20:38.0741894Z 
2025-03-14T05:20:44.8183420Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:20:44.8185207Z loading model: 0it [00:06, ?it/s]
2025-03-14T05:20:44.8198816Z cpu  eval  hf_distil_whisper                  
2025-03-14T05:20:56.0709409Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:21:01.4427873Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:21:06.6848957Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:21:54.6037242Z Compilation time (from dynamo_timed): 40.759299278
2025-03-14T05:21:54.6038245Z pass
2025-03-14T05:21:54.6039267Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:21:54.6040152Z TIMING: _recursive_pre_grad_passes:0.00938 _recursive_joint_graph_passes:0.72396 _recursive_post_grad_passes:0.14987 async_compile.wait:1.39309 code_gen:10.87918 inductor_compile:16.58035 backend_compile:32.51991 entire_frame_compile:40.7593 gc:0.00064 total_wall_time:40.7593
2025-03-14T05:21:54.6041236Z STATS: call_* op count: 635 | FakeTensorMode.__torch_dispatch__:37797 | attempt fast:2568 | fast is_contiguous:2372 | slow no contiguity match:196 | ProxyTorchDispatchMode.__torch_dispatch__:9142 | FakeTensor.__torch_dispatch__:2307
2025-03-14T05:21:54.6041883Z Dynamo produced 1 graphs covering 635 ops with 0 graph breaks (0 unique)
2025-03-14T05:22:00.5284101Z 
2025-03-14T05:22:00.6836176Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:22:00.6837251Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:22:00.6837610Z cpu  eval  lennard_jones                      
2025-03-14T05:22:00.6848665Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:22:00.6873334Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:22:00.6879354Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:22:09.0122774Z Compilation time (from dynamo_timed): 7.425048715
2025-03-14T05:22:09.0123308Z pass
2025-03-14T05:22:09.0123672Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:22:09.0124624Z TIMING: _recursive_pre_grad_passes:0.00295 _recursive_joint_graph_passes:0.10558 _recursive_post_grad_passes:0.01404 async_compile.wait:1.14272 code_gen:6.41057 inductor_compile:6.49815 backend_compile:7.24285 entire_frame_compile:7.42505 gc:0.00036 total_wall_time:7.42505
2025-03-14T05:22:09.0125724Z STATS: call_* op count: 9 | FakeTensor.__torch_dispatch__:25 | FakeTensorMode.__torch_dispatch__:838 | attempt fast:38 | fast is_contiguous:38 | ProxyTorchDispatchMode.__torch_dispatch__:176
2025-03-14T05:22:09.0126280Z Dynamo produced 1 graphs covering 9 ops with 0 graph breaks (0 unique)
2025-03-14T05:22:10.5749908Z accuracy            pass_rate=92.86%
2025-03-14T05:22:10.5753423Z calls_captured      gmean=0.00x mean=411.119x
2025-03-14T05:22:10.5753722Z unique_graphs       gmean=0.00x mean=10.452x
2025-03-14T05:22:10.5753957Z graph_breaks        gmean=0.00x mean=7.881x
2025-03-14T05:22:10.5761687Z unique_graph_breaks gmean=0.00x mean=1.262x
2025-03-14T05:22:10.5761980Z autograd_captures   gmean=0.00x mean=0.000x
2025-03-14T05:22:10.5762215Z autograd_compiles   gmean=0.00x mean=0.000x
2025-03-14T05:22:10.5768350Z cudagraph_skips     gmean=0.00x mean=7.238x
2025-03-14T05:22:10.5768799Z compilation_latency mean=28.996 seconds
2025-03-14T05:22:11.1957187Z + python benchmarks/dynamo/check_accuracy.py --actual /var/lib/jenkins/workspace/test/test-reports/inference_torchbench.csv --expected benchmarks/dynamo/ci_expected_accuracy/dynamic_cpu_inductor_torchbench_inference.csv
2025-03-14T05:22:11.4886651Z BERT_pytorch                        PASS
2025-03-14T05:22:11.4889718Z Background_Matting                  XFAIL
2025-03-14T05:22:11.4890366Z LearningToPaint                     PASS
2025-03-14T05:22:11.4896020Z Super_SloMo                         PASS
2025-03-14T05:22:11.4896269Z alexnet                             PASS
2025-03-14T05:22:11.4911496Z basic_gnn_edgecnn                   PASS
2025-03-14T05:22:11.4911754Z basic_gnn_gcn                       PASS
2025-03-14T05:22:11.4911972Z basic_gnn_gin                       PASS
2025-03-14T05:22:11.4912183Z basic_gnn_sage                      PASS
2025-03-14T05:22:11.4920956Z dcgan                               PASS
2025-03-14T05:22:11.4921308Z demucs                              PASS
2025-03-14T05:22:11.4921627Z densenet121                         PASS
2025-03-14T05:22:11.4921875Z detectron2_fasterrcnn_r_101_c4      PASS
2025-03-14T05:22:11.4922123Z detectron2_fasterrcnn_r_101_dc5     PASS
2025-03-14T05:22:11.4926640Z detectron2_fasterrcnn_r_101_fpn     PASS
2025-03-14T05:22:11.4936060Z detectron2_fasterrcnn_r_50_c4       PASS
2025-03-14T05:22:11.4936440Z detectron2_fasterrcnn_r_50_dc5      PASS
2025-03-14T05:22:11.4939454Z detectron2_fasterrcnn_r_50_fpn      PASS
2025-03-14T05:22:11.4939701Z detectron2_fcos_r_50_fpn            PASS
2025-03-14T05:22:11.4981910Z dlrm                                PASS
2025-03-14T05:22:11.4986053Z doctr_det_predictor                 PASS
2025-03-14T05:22:11.4990866Z doctr_reco_predictor                PASS
2025-03-14T05:22:11.4995572Z drq                                 PASS
2025-03-14T05:22:11.5000548Z fastNLP_Bert                        PASS
2025-03-14T05:22:11.5000932Z functorch_dp_cifar10                PASS
2025-03-14T05:22:11.5005527Z functorch_maml_omniglot             PASS
2025-03-14T05:22:11.5005825Z hf_Albert                           PASS
2025-03-14T05:22:11.5006034Z hf_Bart                             PASS
2025-03-14T05:22:11.5006247Z hf_Bert                             PASS
2025-03-14T05:22:11.5006457Z hf_Bert_large                       PASS
2025-03-14T05:22:11.5006672Z hf_BigBird                          PASS
2025-03-14T05:22:11.5006883Z hf_DistilBert                       PASS
2025-03-14T05:22:11.5007427Z hf_GPT2                             PASS
2025-03-14T05:22:11.5007643Z hf_GPT2_large                       XFAIL
2025-03-14T05:22:11.5007860Z hf_Longformer                       PASS
2025-03-14T05:22:11.5008075Z hf_Reformer                         PASS
2025-03-14T05:22:11.5008285Z hf_Roberta_base                     PASS
2025-03-14T05:22:11.5008492Z hf_T5                               PASS
2025-03-14T05:22:11.5008696Z hf_T5_base                          PASS
2025-03-14T05:22:11.5008905Z hf_T5_large                         XFAIL
2025-03-14T05:22:11.5009123Z hf_distil_whisper                   PASS
2025-03-14T05:22:11.5030699Z lennard_jones                       PASS
2025-03-14T05:22:11.5516010Z + python benchmarks/dynamo/check_graph_breaks.py --actual /var/lib/jenkins/workspace/test/test-reports/inference_torchbench.csv --expected benchmarks/dynamo/ci_expected_accuracy/dynamic_cpu_inductor_torchbench_inference.csv
2025-03-14T05:22:11.8239997Z BERT_pytorch                        PASS
2025-03-14T05:22:11.8240541Z Background_Matting                  PASS
2025-03-14T05:22:11.8246944Z LearningToPaint                     PASS
2025-03-14T05:22:11.8247233Z Super_SloMo                         PASS
2025-03-14T05:22:11.8249032Z alexnet                             PASS
2025-03-14T05:22:11.8249258Z basic_gnn_edgecnn                   PASS
2025-03-14T05:22:11.8257065Z basic_gnn_gcn                       PASS
2025-03-14T05:22:11.8257497Z basic_gnn_gin                       PASS
2025-03-14T05:22:11.8265198Z basic_gnn_sage                      PASS
2025-03-14T05:22:11.8271330Z dcgan                               PASS
2025-03-14T05:22:11.8275293Z demucs                              PASS
2025-03-14T05:22:11.8275698Z densenet121                         PASS
2025-03-14T05:22:11.8276002Z detectron2_fasterrcnn_r_101_c4      PASS
2025-03-14T05:22:11.8282204Z detectron2_fasterrcnn_r_101_dc5     PASS
2025-03-14T05:22:11.8282646Z detectron2_fasterrcnn_r_101_fpn     PASS
2025-03-14T05:22:11.8284248Z detectron2_fasterrcnn_r_50_c4       PASS
2025-03-14T05:22:11.8284661Z detectron2_fasterrcnn_r_50_dc5      PASS
2025-03-14T05:22:11.8287983Z detectron2_fasterrcnn_r_50_fpn      PASS
2025-03-14T05:22:11.8291005Z detectron2_fcos_r_50_fpn            PASS
2025-03-14T05:22:11.8299612Z dlrm                                PASS
2025-03-14T05:22:11.8304387Z doctr_det_predictor                 PASS
2025-03-14T05:22:11.8304812Z doctr_reco_predictor                PASS
2025-03-14T05:22:11.8305051Z drq                                 PASS
2025-03-14T05:22:11.8310808Z fastNLP_Bert                        PASS
2025-03-14T05:22:11.8315684Z functorch_dp_cifar10                PASS
2025-03-14T05:22:11.8316171Z functorch_maml_omniglot             PASS
2025-03-14T05:22:11.8316876Z hf_Albert                           PASS
2025-03-14T05:22:11.8323798Z hf_Bart                             PASS
2025-03-14T05:22:11.8324061Z hf_Bert                             PASS
2025-03-14T05:22:11.8325949Z hf_Bert_large                       PASS
2025-03-14T05:22:11.8328886Z hf_BigBird                          PASS
2025-03-14T05:22:11.8332061Z hf_DistilBert                       PASS
2025-03-14T05:22:11.8341119Z hf_GPT2                             PASS
2025-03-14T05:22:11.8341403Z hf_GPT2_large                       PASS
2025-03-14T05:22:11.8342146Z hf_Longformer                       PASS
2025-03-14T05:22:11.8346816Z hf_Reformer                         PASS
2025-03-14T05:22:11.8352880Z hf_Roberta_base                     PASS
2025-03-14T05:22:11.8353137Z hf_T5                               PASS
2025-03-14T05:22:11.8360433Z hf_T5_base                          PASS
2025-03-14T05:22:11.8360842Z hf_T5_large                         PASS
2025-03-14T05:22:11.8361056Z hf_distil_whisper                   PASS
2025-03-14T05:22:11.8365543Z lennard_jones                       PASS
2025-03-14T05:22:11.8842222Z + cleanup_workspace
2025-03-14T05:22:11.8842907Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.'
2025-03-14T05:22:11.8843838Z sudo may print the following warning message that can be ignored. The chown command will still run.
2025-03-14T05:22:11.8844257Z + echo '    sudo: setrlimit(RLIMIT_STACK): Operation not permitted'
2025-03-14T05:22:11.8844577Z     sudo: setrlimit(RLIMIT_STACK): Operation not permitted
2025-03-14T05:22:11.8844954Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42'
2025-03-14T05:22:11.8845337Z For more details refer to https://github.com/sudo-project/sudo/issues/42
2025-03-14T05:22:11.8845659Z + sudo chown -R 1000 /var/lib/jenkins/workspace
2025-03-14T05:22:12.2437614Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@main
2025-03-14T05:22:12.2437922Z with:
2025-03-14T05:22:12.2438105Z   benchmark-results-dir: test/test-reports
2025-03-14T05:22:12.2438327Z   dry-run: false
2025-03-14T05:22:12.2438498Z   schema-version: v3
2025-03-14T05:22:12.2438872Z   github-token: ***
2025-03-14T05:22:12.2439038Z env:
2025-03-14T05:22:12.2439207Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:12.2439502Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:12.2439810Z ##[endgroup]
2025-03-14T05:22:12.2463033Z ##[group]Run set -eux
2025-03-14T05:22:12.2463240Z set -eux
2025-03-14T05:22:12.2463437Z python3 -mpip install boto3==1.35.33
2025-03-14T05:22:12.2470170Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:12.2470411Z env:
2025-03-14T05:22:12.2470568Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:12.2470897Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:12.2471206Z ##[endgroup]
2025-03-14T05:22:12.2497170Z + python3 -mpip install boto3==1.35.33
2025-03-14T05:22:12.4359594Z Defaulting to user installation because normal site-packages is not writeable
2025-03-14T05:22:13.0952380Z Collecting boto3==1.35.33
2025-03-14T05:22:13.0971695Z   Using cached boto3-1.35.33-py3-none-any.whl (139 kB)
2025-03-14T05:22:13.1019481Z Requirement already satisfied: botocore<1.36.0,>=1.35.33 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.33) (1.35.99)
2025-03-14T05:22:13.1022728Z Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.33) (0.10.4)
2025-03-14T05:22:13.1024621Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.33) (0.10.0)
2025-03-14T05:22:13.1069372Z Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /home/ec2-user/.local/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (2.8.2)
2025-03-14T05:22:13.1078030Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.25.10)
2025-03-14T05:22:13.1107923Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.15.0)
2025-03-14T05:22:13.2321628Z Installing collected packages: boto3
2025-03-14T05:22:13.2321930Z   Attempting uninstall: boto3
2025-03-14T05:22:13.2322154Z     Found existing installation: boto3 1.35.42
2025-03-14T05:22:13.2390011Z     Uninstalling boto3-1.35.42:
2025-03-14T05:22:13.2399525Z       Successfully uninstalled boto3-1.35.42
2025-03-14T05:22:13.2840804Z Successfully installed boto3-1.35.33
2025-03-14T05:22:13.3589577Z ##[group]Run set -eux
2025-03-14T05:22:13.3589796Z set -eux
2025-03-14T05:22:13.3589965Z 
2025-03-14T05:22:13.3590160Z if [[ -z "${GITHUB_TOKEN}" ]]; then
2025-03-14T05:22:13.3590402Z   echo "Missing github-token input"
2025-03-14T05:22:13.3590618Z   exit 1
2025-03-14T05:22:13.3590777Z fi
2025-03-14T05:22:13.3595053Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:13.3595305Z env:
2025-03-14T05:22:13.3595475Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:13.3595780Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:13.3596619Z   GITHUB_TOKEN: ***
2025-03-14T05:22:13.3596807Z ##[endgroup]
2025-03-14T05:22:13.3621555Z + [[ -z *** ]]
2025-03-14T05:22:13.3668707Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main
2025-03-14T05:22:13.3669023Z with:
2025-03-14T05:22:13.3669327Z   github-token: ***
2025-03-14T05:22:13.3669505Z env:
2025-03-14T05:22:13.3669673Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:13.3670148Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:13.3670485Z ##[endgroup]
2025-03-14T05:22:13.3693319Z ##[group]Run set -eux
2025-03-14T05:22:13.3693534Z set -eux
2025-03-14T05:22:13.3693707Z 
2025-03-14T05:22:13.3694037Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}"
2025-03-14T05:22:13.3698658Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:13.3698937Z env:
2025-03-14T05:22:13.3699118Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:13.3699433Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:13.3700112Z   GITHUB_TOKEN: ***
2025-03-14T05:22:13.3700307Z ##[endgroup]
2025-03-14T05:22:13.3722288Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 13849515380 i-00f46405241e0bbc9
2025-03-14T05:22:14.0708816Z setting job-id=38754842362
2025-03-14T05:22:14.0710156Z setting job-name=linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T05:22:14.0820058Z ##[group]Run set -eux
2025-03-14T05:22:14.0820337Z set -eux
2025-03-14T05:22:14.0820510Z 
2025-03-14T05:22:14.0820773Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \
2025-03-14T05:22:14.0821097Z   --schema-version "${SCHEMA_VERSION}" \
2025-03-14T05:22:14.0821354Z   --repo "${REPO}" \
2025-03-14T05:22:14.0821567Z   --head-branch "${HEAD_BRANCH}" \
2025-03-14T05:22:14.0821795Z   --head-sha "${HEAD_SHA}" \
2025-03-14T05:22:14.0822022Z   --workflow-id "${WORKFLOW_RUN_ID}" \
2025-03-14T05:22:14.0822255Z   --run-attempt "${RUN_ATTEMPT}" \
2025-03-14T05:22:14.0822473Z   --job-id "${JOB_ID}" \
2025-03-14T05:22:14.0822679Z   --job-name "${JOB_NAME}"
2025-03-14T05:22:14.0827150Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:14.0827396Z env:
2025-03-14T05:22:14.0827556Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:14.0827852Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:14.0828156Z   SCHEMA_VERSION: v3
2025-03-14T05:22:14.0828333Z   REPO: pytorch/pytorch
2025-03-14T05:22:14.0828554Z   HEAD_BRANCH: refs/heads/main
2025-03-14T05:22:14.0828771Z   HEAD_SHA: aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T05:22:14.0828997Z   WORKFLOW_RUN_ID: 13849515380
2025-03-14T05:22:14.0829181Z   RUN_ATTEMPT: 1
2025-03-14T05:22:14.0829344Z   JOB_ID: 38754842362
2025-03-14T05:22:14.0829672Z   JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T05:22:14.0830021Z ##[endgroup]
2025-03-14T05:22:14.0853600Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch refs/heads/main --head-sha aed0b7a742a2d7b7901790622829cbd2135049a4 --workflow-id 13849515380 --run-attempt 1 --job-id 38754842362 --job-name 'linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)'
2025-03-14T05:22:14.1125614Z ##[group]Run set -eux
2025-03-14T05:22:14.1125830Z set -eux
2025-03-14T05:22:14.1125998Z 
2025-03-14T05:22:14.1126305Z # TODO (huydhn): Implement this part
2025-03-14T05:22:14.1126555Z echo "runners=[]" >> "${GITHUB_OUTPUT}"
2025-03-14T05:22:14.1130985Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:14.1131316Z env:
2025-03-14T05:22:14.1131490Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:14.1131788Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:14.1132096Z ##[endgroup]
2025-03-14T05:22:14.1150335Z + echo 'runners=[]'
2025-03-14T05:22:14.1176549Z ##[group]Run set -eux
2025-03-14T05:22:14.1176872Z set -eux
2025-03-14T05:22:14.1177044Z 
2025-03-14T05:22:14.1177230Z # TODO (huydhn): Implement this part
2025-03-14T05:22:14.1177488Z echo "dependencies={}" >> "${GITHUB_OUTPUT}"
2025-03-14T05:22:14.1181312Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:14.1181560Z env:
2025-03-14T05:22:14.1181726Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:14.1182030Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:14.1182336Z ##[endgroup]
2025-03-14T05:22:14.1201112Z + echo 'dependencies={}'
2025-03-14T05:22:14.1230578Z ##[group]Run set -eux
2025-03-14T05:22:14.1230796Z set -eux
2025-03-14T05:22:14.1230962Z 
2025-03-14T05:22:14.1231155Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then
2025-03-14T05:22:14.1231469Z   echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping"
2025-03-14T05:22:14.1231788Z   # We don't want the job to fail if the directory doesn't exist
2025-03-14T05:22:14.1232035Z   exit 0
2025-03-14T05:22:14.1232194Z fi
2025-03-14T05:22:14.1232348Z 
2025-03-14T05:22:14.1232518Z if [[ "${DRY_RUN}" == "true" ]]; then
2025-03-14T05:22:14.1232821Z   python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \
2025-03-14T05:22:14.1233171Z     --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \
2025-03-14T05:22:14.1233455Z     --metadata "${BENCHMARK_METADATA}" \
2025-03-14T05:22:14.1233686Z     --runners "${RUNNER_INFO}" \
2025-03-14T05:22:14.1233917Z     --dependencies "${DEPENDENCIES}" \
2025-03-14T05:22:14.1234137Z     --dry-run
2025-03-14T05:22:14.1234313Z else
2025-03-14T05:22:14.1234553Z   python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \
2025-03-14T05:22:14.1234886Z     --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \
2025-03-14T05:22:14.1235153Z     --metadata "${BENCHMARK_METADATA}" \
2025-03-14T05:22:14.1235385Z     --runners "${RUNNER_INFO}" \
2025-03-14T05:22:14.1235613Z     --dependencies "${DEPENDENCIES}"
2025-03-14T05:22:14.1235826Z fi
2025-03-14T05:22:14.1240095Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:14.1240351Z env:
2025-03-14T05:22:14.1240520Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:14.1240832Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:14.1241164Z   BENCHMARK_RESULTS_DIR: test/test-reports
2025-03-14T05:22:14.1241377Z   DRY_RUN: false
2025-03-14T05:22:14.1242187Z   BENCHMARK_METADATA: {"timestamp": 1741929734, "schema_version": "v3", "name": "linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "aed0b7a742a2d7b7901790622829cbd2135049a4", "workflow_id": 13849515380, "run_attempt": 1, "job_id": 38754842362}
2025-03-14T05:22:14.1242999Z   RUNNER_INFO: []
2025-03-14T05:22:14.1243169Z   DEPENDENCIES: {}
2025-03-14T05:22:14.1243337Z ##[endgroup]
2025-03-14T05:22:14.1263089Z + [[ ! -d test/test-reports ]]
2025-03-14T05:22:14.1265076Z + [[ false == \t\r\u\e ]]
2025-03-14T05:22:14.1266520Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py --benchmark-results-dir test/test-reports --metadata '{"timestamp": 1741929734, "schema_version": "v3", "name": "linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "aed0b7a742a2d7b7901790622829cbd2135049a4", "workflow_id": 13849515380, "run_attempt": 1, "job_id": 38754842362}' --runners '[]' --dependencies '{}'
2025-03-14T05:22:14.2453917Z INFO:root:Upload test/test-reports/inference_torchbench.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13849515380/38754842362/inference_torchbench.json
2025-03-14T05:22:14.2704256Z INFO:botocore.credentials:Found credentials from IAM Role: gh-ci-github-action-runners-runner-role
2025-03-14T05:22:14.4498922Z INFO:root:Upload test/test-reports/inference_torchbench_graph_breaks.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13849515380/38754842362/inference_torchbench_graph_breaks.json
2025-03-14T05:22:14.6692273Z ##[group]Run cat test/**/*_toprint.log || true
2025-03-14T05:22:14.6692585Z cat test/**/*_toprint.log || true
2025-03-14T05:22:14.6697204Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:14.6697461Z env:
2025-03-14T05:22:14.6697635Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:14.6697941Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:14.6698260Z ##[endgroup]
2025-03-14T05:22:14.6755305Z cat: 'test/**/*_toprint.log': No such file or directory
2025-03-14T05:22:14.6786283Z ##[group]Run kill "$MONITOR_SCRIPT_PID"
2025-03-14T05:22:14.6786546Z kill "$MONITOR_SCRIPT_PID"
2025-03-14T05:22:14.6790441Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:14.6790689Z env:
2025-03-14T05:22:14.6790859Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:14.6791149Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:14.6791484Z   MONITOR_SCRIPT_PID: 225438
2025-03-14T05:22:14.6791675Z ##[endgroup]
2025-03-14T05:22:14.6883393Z Prepare all required actions
2025-03-14T05:22:14.6883756Z Getting action download info
2025-03-14T05:22:14.8238524Z Download action repository 'actions/upload-artifact@v4' (SHA:4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1)
2025-03-14T05:22:15.3069011Z ##[group]Run ./.github/actions/upload-test-artifacts
2025-03-14T05:22:15.3069280Z with:
2025-03-14T05:22:15.3069568Z   file-suffix: test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754842362
2025-03-14T05:22:15.3069895Z   s3-bucket: gha-artifacts
2025-03-14T05:22:15.3070087Z env:
2025-03-14T05:22:15.3070252Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:15.3070553Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:15.3070868Z ##[endgroup]
2025-03-14T05:22:15.3097383Z ##[group]Run # Remove any previous test jsons if they exist
2025-03-14T05:22:15.3097717Z # Remove any previous test jsons if they exist
2025-03-14T05:22:15.3097999Z rm -f test-jsons-*.zip
2025-03-14T05:22:15.3098344Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json'
2025-03-14T05:22:15.3102830Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:15.3103091Z env:
2025-03-14T05:22:15.3103269Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:15.3103586Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:15.3104026Z   FILE_SUFFIX: test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754842362
2025-03-14T05:22:15.3104348Z ##[endgroup]
2025-03-14T05:22:15.3197025Z   adding: test/test-reports/inference_torchbench.json (deflated 99%)
2025-03-14T05:22:15.3348286Z   adding: test/test-reports/inference_torchbench_graph_breaks.json (deflated 99%)
2025-03-14T05:22:15.3377213Z ##[group]Run # Remove any previous test reports if they exist
2025-03-14T05:22:15.3377531Z # Remove any previous test reports if they exist
2025-03-14T05:22:15.3377894Z rm -f test-reports-*.zip
2025-03-14T05:22:15.3378220Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv'
2025-03-14T05:22:15.3382380Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:15.3382636Z env:
2025-03-14T05:22:15.3382800Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:15.3383091Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:15.3383489Z   FILE_SUFFIX: test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754842362
2025-03-14T05:22:15.3383776Z ##[endgroup]
2025-03-14T05:22:15.3421155Z   adding: test/test-reports/inference_torchbench.csv (deflated 62%)
2025-03-14T05:22:15.3434194Z   adding: test/test-reports/inference_torchbench_graph_breaks.csv (deflated 98%)
2025-03-14T05:22:15.3434941Z   adding: test/test-reports/inference_torchbench_graph_break_deduped.csv (deflated 81%)
2025-03-14T05:22:15.3461134Z ##[group]Run # Remove any previous usage logs if they exist
2025-03-14T05:22:15.3461494Z # Remove any previous usage logs if they exist
2025-03-14T05:22:15.3461754Z rm -f logs-*.zip
2025-03-14T05:22:15.3462064Z # this workflow is also run in bazel build test, but we dont generate usage reports for it
2025-03-14T05:22:15.3462413Z # so check to see if the file exists first
2025-03-14T05:22:15.3462663Z if [ -f 'usage_log.txt' ]; then
2025-03-14T05:22:15.3462926Z     zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt'
2025-03-14T05:22:15.3463167Z fi
2025-03-14T05:22:15.3463428Z if find "test/test-reports" -name "*.log" 2>/dev/null | grep -q .; then
2025-03-14T05:22:15.3463786Z     zip -r "logs-${FILE_SUFFIX}.zip" test/test-reports -i '*.log'
2025-03-14T05:22:15.3464052Z fi
2025-03-14T05:22:15.3468120Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:15.3468358Z env:
2025-03-14T05:22:15.3468517Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:15.3468942Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:15.3469368Z   FILE_SUFFIX: test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754842362
2025-03-14T05:22:15.3469660Z ##[endgroup]
2025-03-14T05:22:15.3524790Z   adding: usage_log.txt (deflated 96%)
2025-03-14T05:22:15.3562958Z ##[group]Run # Remove any previous debugging artifacts if they exist
2025-03-14T05:22:15.3563295Z # Remove any previous debugging artifacts if they exist
2025-03-14T05:22:15.3563556Z rm -f debug-*.zip
2025-03-14T05:22:15.3563753Z if [ -d 'test/debug' ]; then
2025-03-14T05:22:15.3563992Z   zip -r "debug-${FILE_SUFFIX}.zip" test/debug
2025-03-14T05:22:15.3564214Z fi
2025-03-14T05:22:15.3568607Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:15.3568854Z env:
2025-03-14T05:22:15.3569023Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:15.3569346Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:15.3569785Z   FILE_SUFFIX: test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754842362
2025-03-14T05:22:15.3570096Z ##[endgroup]
2025-03-14T05:22:15.3643273Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-14T05:22:15.3643504Z with:
2025-03-14T05:22:15.3643674Z   s3-bucket: gha-artifacts
2025-03-14T05:22:15.3643902Z   s3-prefix: pytorch/pytorch/13849515380/1/artifact

2025-03-14T05:22:15.3644134Z   retention-days: 14
2025-03-14T05:22:15.3644326Z   if-no-files-found: warn
2025-03-14T05:22:15.3644517Z   path: test-jsons-*.zip
2025-03-14T05:22:15.3644694Z   name: artifact
2025-03-14T05:22:15.3644861Z   region: us-east-1
2025-03-14T05:22:15.3645027Z env:
2025-03-14T05:22:15.3645184Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:15.3645480Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:15.3645784Z ##[endgroup]
2025-03-14T05:22:15.6530356Z NOTE: s3-prefix specified, ignoring name parameter
2025-03-14T05:22:15.6530915Z With the provided path, there will be 1 file uploaded
2025-03-14T05:22:15.6531352Z Uploading to s3 prefix: pytorch/pytorch/13849515380/1/artifact
2025-03-14T05:22:15.6557068Z Starting upload of test-jsons-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754842362.zip
2025-03-14T05:22:15.7860050Z Finished upload of test-jsons-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754842362.zip
2025-03-14T05:22:15.8018884Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-14T05:22:15.8019135Z with:
2025-03-14T05:22:15.8019321Z   s3-bucket: gha-artifacts
2025-03-14T05:22:15.8019563Z   s3-prefix: pytorch/pytorch/13849515380/1/artifact

2025-03-14T05:22:15.8019811Z   retention-days: 14
2025-03-14T05:22:15.8020004Z   if-no-files-found: error
2025-03-14T05:22:15.8020207Z   path: test-reports-*.zip
2025-03-14T05:22:15.8020398Z   name: artifact
2025-03-14T05:22:15.8020570Z   region: us-east-1
2025-03-14T05:22:15.8020761Z env:
2025-03-14T05:22:15.8020919Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:15.8021261Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:15.8021597Z ##[endgroup]
2025-03-14T05:22:16.0520430Z NOTE: s3-prefix specified, ignoring name parameter
2025-03-14T05:22:16.0520943Z With the provided path, there will be 1 file uploaded
2025-03-14T05:22:16.0521369Z Uploading to s3 prefix: pytorch/pytorch/13849515380/1/artifact
2025-03-14T05:22:16.0545413Z Starting upload of test-reports-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754842362.zip
2025-03-14T05:22:16.1519711Z Finished upload of test-reports-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754842362.zip
2025-03-14T05:22:16.1698780Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-14T05:22:16.1699021Z with:
2025-03-14T05:22:16.1699197Z   s3-bucket: gha-artifacts
2025-03-14T05:22:16.1699423Z   s3-prefix: pytorch/pytorch/13849515380/1/artifact

2025-03-14T05:22:16.1699669Z   retention-days: 14
2025-03-14T05:22:16.1699846Z   if-no-files-found: ignore
2025-03-14T05:22:16.1700168Z   path: logs-*.zip
2025-03-14T05:22:16.1700340Z   name: artifact
2025-03-14T05:22:16.1700501Z   region: us-east-1
2025-03-14T05:22:16.1700667Z env:
2025-03-14T05:22:16.1700842Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:16.1701151Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:16.1701456Z ##[endgroup]
2025-03-14T05:22:16.4230690Z NOTE: s3-prefix specified, ignoring name parameter
2025-03-14T05:22:16.4236003Z With the provided path, there will be 1 file uploaded
2025-03-14T05:22:16.4238141Z Uploading to s3 prefix: pytorch/pytorch/13849515380/1/artifact
2025-03-14T05:22:16.4253556Z Starting upload of logs-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754842362.zip
2025-03-14T05:22:16.5511758Z Finished upload of logs-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754842362.zip
2025-03-14T05:22:16.5698799Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-14T05:22:16.5699067Z with:
2025-03-14T05:22:16.5699261Z   s3-bucket: gha-artifacts
2025-03-14T05:22:16.5699504Z   s3-prefix: pytorch/pytorch/13849515380/1/artifact

2025-03-14T05:22:16.5699755Z   retention-days: 14
2025-03-14T05:22:16.5699949Z   if-no-files-found: ignore
2025-03-14T05:22:16.5700138Z   path: debug-*.zip
2025-03-14T05:22:16.5700304Z   name: artifact
2025-03-14T05:22:16.5700461Z   region: us-east-1
2025-03-14T05:22:16.5700623Z env:
2025-03-14T05:22:16.5700780Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:16.5701077Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:16.5701382Z ##[endgroup]
2025-03-14T05:22:16.8138062Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded.
2025-03-14T05:22:16.8314925Z ##[group]Run # shellcheck disable=SC2156
2025-03-14T05:22:16.8315195Z # shellcheck disable=SC2156
2025-03-14T05:22:16.8315575Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \;
2025-03-14T05:22:16.8320238Z shell: /usr/bin/bash -e {0}
2025-03-14T05:22:16.8320482Z env:
2025-03-14T05:22:16.8320660Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:16.8320964Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:16.8321282Z ##[endgroup]
2025-03-14T05:22:16.9667300Z Prepare all required actions
2025-03-14T05:22:16.9667635Z Getting action download info
2025-03-14T05:22:17.0554168Z ##[group]Run ./.github/actions/upload-utilization-stats
2025-03-14T05:22:17.0554422Z with:
2025-03-14T05:22:17.0554591Z   job_id: 38754842362
2025-03-14T05:22:17.0554938Z   job_name: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T05:22:17.0555291Z   workflow_name: inductor
2025-03-14T05:22:17.0555475Z   workflow_run_id: 13849515380
2025-03-14T05:22:17.0555665Z   workflow_attempt: 1
2025-03-14T05:22:17.0555847Z env:
2025-03-14T05:22:17.0555998Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:17.0556285Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:17.0556581Z ##[endgroup]
2025-03-14T05:22:17.0575392Z ##[group]Run echo "workflow_id: 13849515380"
2025-03-14T05:22:17.0575654Z echo "workflow_id: 13849515380"
2025-03-14T05:22:17.0575902Z echo "workflow_attempt: 1"
2025-03-14T05:22:17.0576118Z echo "workflow_Name: inductor"
2025-03-14T05:22:17.0576324Z echo "job_id: 38754842362"
2025-03-14T05:22:17.0576702Z echo "job_name:  linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)"
2025-03-14T05:22:17.0581864Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:17.0582121Z env:
2025-03-14T05:22:17.0582292Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:17.0582590Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:17.0582910Z ##[endgroup]
2025-03-14T05:22:17.0605340Z workflow_id: 13849515380
2025-03-14T05:22:17.0607224Z workflow_attempt: 1
2025-03-14T05:22:17.0607560Z workflow_Name: inductor
2025-03-14T05:22:17.0607833Z job_id: 38754842362
2025-03-14T05:22:17.0608251Z job_name:  linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T05:22:17.0638915Z ##[group]Run nick-fields/retry@v3.0.0
2025-03-14T05:22:17.0639132Z with:
2025-03-14T05:22:17.0639289Z   shell: bash
2025-03-14T05:22:17.0639448Z   timeout_minutes: 5
2025-03-14T05:22:17.0639617Z   max_attempts: 5
2025-03-14T05:22:17.0639785Z   retry_wait_seconds: 30
2025-03-14T05:22:17.0640079Z   command: set -eu
python3 -m pip install python-dateutil==2.8.2 boto3==1.35.42 pandas==2.1.3

2025-03-14T05:22:17.0640387Z   polling_interval_seconds: 1
2025-03-14T05:22:17.0640573Z   warning_on_retry: true
2025-03-14T05:22:17.0640751Z   continue_on_error: false
2025-03-14T05:22:17.0640924Z env:
2025-03-14T05:22:17.0641080Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:17.0641364Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:17.0641663Z ##[endgroup]
2025-03-14T05:22:17.3106135Z Defaulting to user installation because normal site-packages is not writeable
2025-03-14T05:22:17.3235680Z Requirement already satisfied: python-dateutil==2.8.2 in /home/ec2-user/.local/lib/python3.9/site-packages (2.8.2)
2025-03-14T05:22:17.9582662Z Collecting boto3==1.35.42
2025-03-14T05:22:17.9594489Z   Using cached boto3-1.35.42-py3-none-any.whl (139 kB)
2025-03-14T05:22:17.9617153Z Requirement already satisfied: pandas==2.1.3 in /home/ec2-user/.local/lib/python3.9/site-packages (2.1.3)
2025-03-14T05:22:17.9628542Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil==2.8.2) (1.15.0)
2025-03-14T05:22:17.9659571Z Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.4)
2025-03-14T05:22:17.9661012Z Requirement already satisfied: botocore<1.36.0,>=1.35.42 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (1.35.99)
2025-03-14T05:22:17.9668186Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.0)
2025-03-14T05:22:18.0320605Z Requirement already satisfied: tzdata>=2022.1 in /home/ec2-user/.local/lib/python3.9/site-packages (from pandas==2.1.3) (2025.1)
2025-03-14T05:22:18.0321261Z Requirement already satisfied: numpy<2,>=1.22.4 in /home/ec2-user/.local/lib/python3.9/site-packages (from pandas==2.1.3) (1.26.4)
2025-03-14T05:22:18.0321804Z Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3.9/site-packages (from pandas==2.1.3) (2022.7.1)
2025-03-14T05:22:18.0364841Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.42->boto3==1.35.42) (1.25.10)
2025-03-14T05:22:18.0921665Z Installing collected packages: boto3
2025-03-14T05:22:18.0922223Z   Attempting uninstall: boto3
2025-03-14T05:22:18.0922523Z     Found existing installation: boto3 1.35.33
2025-03-14T05:22:18.0984907Z     Uninstalling boto3-1.35.33:
2025-03-14T05:22:18.0992444Z       Successfully uninstalled boto3-1.35.33
2025-03-14T05:22:18.1399271Z Successfully installed boto3-1.35.42
2025-03-14T05:22:19.1259360Z Command completed after 1 attempt(s).
2025-03-14T05:22:19.1317073Z ##[group]Run python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \
2025-03-14T05:22:19.1317525Z python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \
2025-03-14T05:22:19.1317853Z   --workflow-run-id "13849515380" \
2025-03-14T05:22:19.1318091Z   --workflow-name "inductor" \
2025-03-14T05:22:19.1318320Z   --workflow-run-attempt "1" \
2025-03-14T05:22:19.1318535Z   --job-id "38754842362" \
2025-03-14T05:22:19.1318930Z   --job-name "linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)"
2025-03-14T05:22:19.1324283Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:19.1324543Z env:
2025-03-14T05:22:19.1324721Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:19.1325035Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:19.1325358Z ##[endgroup]
2025-03-14T05:22:20.0947901Z repo: pytorch/pytorch
2025-03-14T05:22:20.0949748Z Downloading logs-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754842362.zip
2025-03-14T05:22:20.0950130Z Converted Log Model: UtilizationMetadata:
2025-03-14T05:22:20.0955422Z  UtilizationMetadata(level='metadata', workflow_id='13849515380', job_id='38754842362', workflow_name='inductor', job_name='linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)', usage_collect_interval=1.0, data_model_version=1.0, start_at=1741925871, gpu_count=0, cpu_count=32, gpu_type='', error=None)
2025-03-14T05:22:20.0957002Z [Db Segments] detected pytest cmd: 15, generated segments: 15
2025-03-14T05:22:20.0957312Z [db model] Peek db timeseries 
2025-03-14T05:22:20.0957506Z :{
2025-03-14T05:22:20.0957661Z     "created_at": 1741929739,
2025-03-14T05:22:20.0957858Z     "type": "utilization",
2025-03-14T05:22:20.0958040Z     "tags": [
2025-03-14T05:22:20.0958223Z         "record"
2025-03-14T05:22:20.0958382Z     ],
2025-03-14T05:22:20.0958541Z     "time_stamp": 1741925871,
2025-03-14T05:22:20.0958732Z     "repo": "pytorch/pytorch",
2025-03-14T05:22:20.0958923Z     "workflow_id": 13849515380,
2025-03-14T05:22:20.0959112Z     "run_attempt": 1,
2025-03-14T05:22:20.0959277Z     "job_id": 38754842362,
2025-03-14T05:22:20.0959464Z     "workflow_name": "inductor",
2025-03-14T05:22:20.0959833Z     "job_name": "linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)",
2025-03-14T05:22:20.0960183Z     "json_data": "{}"
2025-03-14T05:22:20.0960940Z }
2025-03-14T05:22:20.0961273Z Writing 1 documents to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13849515380/1/38754842362/metadata
2025-03-14T05:22:20.0961788Z Done! Finish writing document to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13849515380/1/38754842362/metadata 
2025-03-14T05:22:20.0962439Z Writing 760 documents to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13849515380/1/38754842362/time_series
2025-03-14T05:22:20.0962978Z Done! Finish writing document to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13849515380/1/38754842362/time_series 
2025-03-14T05:22:20.1943173Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main
2025-03-14T05:22:20.1943484Z with:
2025-03-14T05:22:20.1943654Z env:
2025-03-14T05:22:20.1943837Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:20.1944136Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:20.1944439Z ##[endgroup]
2025-03-14T05:22:20.1963010Z ##[group]Run set -eou pipefail
2025-03-14T05:22:20.1963285Z set -eou pipefail
2025-03-14T05:22:20.1963487Z 
2025-03-14T05:22:20.1963749Z echo "Holding runner for 2 hours until all ssh sessions have logged out"
2025-03-14T05:22:20.1964060Z for _ in $(seq 1440); do
2025-03-14T05:22:20.1964299Z     # Break if no ssh session exists anymore
2025-03-14T05:22:20.1964544Z     if [ "$(who)" = "" ]; then
2025-03-14T05:22:20.1964814Z       break
2025-03-14T05:22:20.1964995Z     fi
2025-03-14T05:22:20.1965216Z     echo "."
2025-03-14T05:22:20.1965404Z     sleep 5
2025-03-14T05:22:20.1965588Z done
2025-03-14T05:22:20.1970167Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:20.1970427Z env:
2025-03-14T05:22:20.1970602Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:20.1970919Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:20.1971348Z ##[endgroup]
2025-03-14T05:22:20.1995875Z Holding runner for 2 hours until all ssh sessions have logged out
2025-03-14T05:22:20.2044289Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2025-03-14T05:22:20.2044651Z # ignore expansion of "docker ps -q" since it could be empty
2025-03-14T05:22:20.2044925Z # shellcheck disable=SC2046
2025-03-14T05:22:20.2045159Z docker stop $(docker ps -q) || true
2025-03-14T05:22:20.2045399Z # Prune all of the docker images
2025-03-14T05:22:20.2045624Z docker system prune -af
2025-03-14T05:22:20.2049541Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:20.2049804Z env:
2025-03-14T05:22:20.2049979Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:22:20.2050287Z   DOCKER_CONTAINER_ID: f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:20.2050606Z ##[endgroup]
2025-03-14T05:22:30.8571881Z f121771cb12d
2025-03-14T05:22:31.9889538Z Deleted Containers:
2025-03-14T05:22:31.9889934Z f121771cb12d163c30755f1e5289de1efd56b3dd586381a77faf33b8cb14ca05
2025-03-14T05:22:31.9890163Z 
2025-03-14T05:22:35.5138277Z Deleted Images:
2025-03-14T05:22:35.5138944Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T05:22:35.5139870Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks@sha256:2f16eb7d476b5dc359eb789543b0cfc9aa5c04fe105d51acd219f91259bad5ab
2025-03-14T05:22:35.5140594Z deleted: sha256:1bab961b3bd90ec6714167a095e5437a0239700971887d1c663377ef708f8526
2025-03-14T05:22:35.5141012Z deleted: sha256:f1c70fafa21653c183da3a27b152e3132edfcc8f536568e599a8a7475a898486
2025-03-14T05:22:35.5141412Z deleted: sha256:257be7015a258302c070c7e2128106874db3320b42c0be587cde8a5c4668eb59
2025-03-14T05:22:35.5141811Z deleted: sha256:948eebb525eda26cfb174529301ea9c21fa30d7c38274102a4e84e8c5fbe8384
2025-03-14T05:22:35.5142488Z deleted: sha256:b9ac7767cf0b17526969b4b0803e8c07d3e794c502cfc420582474375cd9a813
2025-03-14T05:22:35.5143418Z deleted: sha256:80c0b71da98feea65ea51c56635d7acb37ad1eefb862adffe484b0a6ec73d382
2025-03-14T05:22:35.5143829Z deleted: sha256:9211eaf972d93a6b3e1157ed0733c6ef570902faf25a90ffcca263c3ecfe6a71
2025-03-14T05:22:35.5144391Z deleted: sha256:770a3a228550db1ce19da1d84c8ab8f7422a2d6b2177a744f04869331fdc011f
2025-03-14T05:22:35.5144773Z deleted: sha256:ca765c3fb3c14963c38334372443d928ed9dd142026ae5d3163a46c734f59f17
2025-03-14T05:22:35.5145316Z deleted: sha256:c693226c9ece102dfdf509e7a30f3389f47d01eb5736741dd7e5fa024b375eb6
2025-03-14T05:22:35.5145698Z deleted: sha256:d1f6e2d14a7b04693929aea0e661fb71482e5aa1b532a7125191965991fda143
2025-03-14T05:22:35.5146073Z deleted: sha256:07993bec321dd3bb3b1494aaa59c9394f9fcc3410757bd99f69589239a44725f
2025-03-14T05:22:35.5146449Z deleted: sha256:055322257b5cf41cb1a718868e89f759cdd0ff049e292ac7ef1d2a53ca86becd
2025-03-14T05:22:35.5146818Z deleted: sha256:252ed6634b114d7f66233d28c6ad76465b3644f087d340899c7e951999871bdc
2025-03-14T05:22:35.5147194Z deleted: sha256:1f4bc6481e6b53fcffa3231415bb421a166650c5c85cc8815fc631940944134d
2025-03-14T05:22:35.5147573Z deleted: sha256:757e553e71a5e40e7d79542c8fba96d32a5d5d2cc83dabe0a0873d2ae30a6760
2025-03-14T05:22:35.5148371Z deleted: sha256:c73d008283448cd7f9af3b7e93fa7224c699fb8bf57ea48bfa9bfa70b7d0899c
2025-03-14T05:22:35.5151751Z deleted: sha256:8f1f72b72763b40f294d9a61548ab4361197ad009b424337cbbab4b686e31b5e
2025-03-14T05:22:35.5152130Z deleted: sha256:457542c336f851527a24474a65464edf810bb01fc59d14a134de0966f2da22b5
2025-03-14T05:22:35.5152500Z deleted: sha256:0b068011d230ca630e8e88da9706397abab719e1e0f01458c050ab3dc266fb69
2025-03-14T05:22:35.5152875Z deleted: sha256:6b7611c1a0fe36fb64f6f38852ab7dd7128544ebd5b79fd1dae59588d01f1999
2025-03-14T05:22:35.5153247Z deleted: sha256:58418894fc257c7e5594b550bb7984c65bc6d459312f9a9ea6455c2a1ff7913f
2025-03-14T05:22:35.5153609Z deleted: sha256:9b8936eac0cf72c19f68e982a4114c32058b6f70c748a778848403779f2656a7
2025-03-14T05:22:35.5153981Z deleted: sha256:4f5c047819041836387aae08ee2b5f13239a3994f0ea2320ef272b2478dc8ef4
2025-03-14T05:22:35.5154356Z deleted: sha256:ed9af6f191cd384d7fe4ba6ff77a2314ce8f02d6eb65f4c22af24979dcab65f9
2025-03-14T05:22:35.5154732Z deleted: sha256:147d2ba7ff1f79138609e78e537e545abf74b6d35f8ce6e7631cd947cc97e4eb
2025-03-14T05:22:35.5155102Z deleted: sha256:c0383887a64b287af7ffb85f9e90ca26024838dee5135f0daa0f521e3c9f4700
2025-03-14T05:22:35.5155466Z deleted: sha256:d458cfd4d3050444c825537087bc07c1959e7fb8a3e339e109479a808f9aa3d7
2025-03-14T05:22:35.5155841Z deleted: sha256:185e72f4c05edf1cfb429debc32253ec9d900149e814f8109a0487b439c6499d
2025-03-14T05:22:35.5156217Z deleted: sha256:35a932e808caf02132ebe5708af4779e3a3bbb0d0f0d9dd768e042700dd6f1ac
2025-03-14T05:22:35.5156593Z deleted: sha256:b7ab9bb1ef36a3632cc73431e5f66d93a8a6b8606ceaac43a6530aef41d043be
2025-03-14T05:22:35.5156970Z deleted: sha256:98446da4367bce4c693b76a2c0dfe3e5d92337c68c3628411ed5a9d3e75e3001
2025-03-14T05:22:35.5157348Z deleted: sha256:2fa7934e407325f3915810fc60f7264a0e37d1fc3025dfe317ad47dfa736a596
2025-03-14T05:22:35.5158453Z deleted: sha256:5826059d955a5b42eb58fac9d3b728ea9b86c24950e455b575d0d675595fdb83
2025-03-14T05:22:35.5158847Z deleted: sha256:d0c120734162460fdbec1bc231b58beb664f44f28eb4faa45148425510538786
2025-03-14T05:22:35.5159767Z deleted: sha256:694a490b82d77b3cd2d35e952e32c6faad294ddc6437f5a3177f77b1e3c7617d
2025-03-14T05:22:35.5160147Z deleted: sha256:8098d7ec4fc850921ae016dac5183c08eac3cfdd64e5c4fb59f8bd4edf7db18b
2025-03-14T05:22:35.5160706Z deleted: sha256:e3ba634c3ec74783be198d136bf23bb929a7fb93e62ab20a1bcefd82c944dae3
2025-03-14T05:22:35.5161099Z deleted: sha256:6c787593e1088a677c18a8428fb718af38a8730c671b85e4ed3a15ad64cc655d
2025-03-14T05:22:35.5161509Z deleted: sha256:e513913d353704462a90df83aac640c6c5d894dd4dc2651954be291287b8c22b
2025-03-14T05:22:35.5161888Z deleted: sha256:a191277f29357fe043dd2122c9a4b78cd3ed9bfb92e7963cca9a9d101f575ba6
2025-03-14T05:22:35.5162277Z deleted: sha256:59f43ce6f7edc380dcc444b56e00f2dc9ef23196590e453e73e68295498f25b5
2025-03-14T05:22:35.5162817Z deleted: sha256:9e059d039c42b907d3b772990000dd155e0a8e82727705db50bd91a3fee87f72
2025-03-14T05:22:35.5163198Z deleted: sha256:369472d11d5b1bd72071cb5c2e5098082c7d7fc672aa13bd89d7102ef120fb14
2025-03-14T05:22:35.5163583Z deleted: sha256:2d910764023ddf5d12701abdd2fee664955fc097db5f24da2c109f3fc9ae07e8
2025-03-14T05:22:35.5163975Z deleted: sha256:e9b8a74eae78cdde6378061a2db467b6576d02ad41f9be215b0d9caca6e38aff
2025-03-14T05:22:35.5164500Z deleted: sha256:2dba6a95777543f0c4d7f5438367153b3d413e3fe0a67c67f5c616a48b52513a
2025-03-14T05:22:35.5164887Z deleted: sha256:f6342f36954b412d64fa5fcfeed36153e1864fb91a07d355a86421f24025fb26
2025-03-14T05:22:35.5165270Z deleted: sha256:2f42c8962a9681df48e3f6db2b6aebc67070be224ce48397934c362230e7b25a
2025-03-14T05:22:35.5165659Z deleted: sha256:7b16cb1a08ae455e502b2cc5eb9797f88ae1065ac3ef10d58b07cbc92320abed
2025-03-14T05:22:35.5166039Z deleted: sha256:53f49fe06b7e72120214392e1d0b5e349602569da8dd320124014e47170b1443
2025-03-14T05:22:35.5166423Z deleted: sha256:ab72286eb578c1e0ebb7f93a5033a70fa2987820d475ac58b94b9cda1d97d1f8
2025-03-14T05:22:35.5166810Z deleted: sha256:0f2284231d71ecf5b661c70c76f59235283ebea2109eeb9c930f28de80c25db7
2025-03-14T05:22:35.5167187Z deleted: sha256:c17a9c11f5fa0dd2fb6aa76c4d226ca4920a4814da35d11a23132c8cddea56de
2025-03-14T05:22:35.5167572Z deleted: sha256:5e7b289a68c88c68aaae9f774d75ef2c3560726dcb0bb68c40b83476f5279fc3
2025-03-14T05:22:35.5167959Z deleted: sha256:54383c657e0814e9df4dfeb9e439b4e9d961ea5567b5a8dd1fb8dcd274e6a542
2025-03-14T05:22:35.5168347Z deleted: sha256:23c598cb49f1778286c6dd9d3ad8fd6a5dda3723759aff290c57b9b4b33a5254
2025-03-14T05:22:35.5168725Z deleted: sha256:151e4be891c683a58b369e2a381d56d890726f130c1034198bafa486874d5f3c
2025-03-14T05:22:35.5169101Z deleted: sha256:67d27664ba27792cc2a3be862a7785db4b2e94e90cd475f1b4c3583420af54b2
2025-03-14T05:22:35.5169484Z deleted: sha256:444db72c2c6410fa918f5239d9f40f8d60bacc2a930c1d2e88e55ba7e90dd190
2025-03-14T05:22:35.5169883Z deleted: sha256:e5956c73e6ed7735c73c96aedb3facef946e7d8e8c7d575586f3984e2a088cd3
2025-03-14T05:22:35.5170277Z deleted: sha256:1161ba2320dc98f34e89e2b2cb247532b5b751d06388cef74c3d223d91c8a54f
2025-03-14T05:22:35.5170670Z deleted: sha256:636533db6e096cfee9d15fccffa860f852e36a17de826d1faa6437a8b4c2f07c
2025-03-14T05:22:35.5171059Z deleted: sha256:f17858d33b62b794fe3b08f18497295725959fce7165892d91022e21f4a58d35
2025-03-14T05:22:35.5171571Z deleted: sha256:f55340aa88fce044fd887201e56e51a56ca87457b6a9f6f9a9a62f80747cb592
2025-03-14T05:22:35.5171978Z deleted: sha256:c613727faed9b4df57f35fd72a70cc58fd88381934d9d791a2df9f7eb487949c
2025-03-14T05:22:35.5172388Z deleted: sha256:5202841bb151e8fbce051a8b8b8f4f5b7e85ecb6c850b69b598b89a274183357
2025-03-14T05:22:35.5172776Z deleted: sha256:d7be908f71f3b9bf7e4a187792e5bac7003dcd822e6cb7b37e8433357a424385
2025-03-14T05:22:35.5173167Z deleted: sha256:449c13d02298fc8bc6feed4fb489f32abb05d2b75276f53b3420f21ef4e5491b
2025-03-14T05:22:35.5173556Z deleted: sha256:f31347f63f35b34e452b270d28b824e9a4e2d0253c13f1dca0ddf8a215f44f00
2025-03-14T05:22:35.5173942Z deleted: sha256:807f08b2b423031a7d17dbbcd56df41066b0120a860db8bbb66d27623edc912d
2025-03-14T05:22:35.5174328Z deleted: sha256:9d014db3c35f5c903d5ec3aeedc2eb3f569b530ff55db80068e67279f389df4a
2025-03-14T05:22:35.5179209Z deleted: sha256:8ec9904b6ea8685f42923ea8255de513a06216a8d79d3e3ccda1a784f8bd87b3
2025-03-14T05:22:35.5179705Z deleted: sha256:d52c246bd07bfddac4b07ce20e1a71945bae916d4368fb5d428399401af72671
2025-03-14T05:22:35.5180131Z deleted: sha256:d3cc357f8bbe14bbbe61457d5b4f8965a24bb075da1e5587aac9ae3c795a8bcd
2025-03-14T05:22:35.5180611Z deleted: sha256:26b82bcc6a1cc00d5754a8546f1dcea0af13297161ad9c1ae9e69eb5ad77f45c
2025-03-14T05:22:35.5181004Z deleted: sha256:286d6fa3b60884a3f41e875ae31941ea7e573a833e6583fcc4322f6fe1f517a1
2025-03-14T05:22:35.5181389Z deleted: sha256:ce07ff180683bb03ff3bc34cd53532e4e226c1c315e6098a00fe6cef4a27778c
2025-03-14T05:22:35.5181779Z deleted: sha256:53f0f725132031f901b12e35d8c9cf4acaa8e2a2fd0ef6d5cde2300b948c8d6c
2025-03-14T05:22:35.5182307Z deleted: sha256:c5bf6f7b5a9c3ea1474f8c18b9576ee713beedd36681d88737b27cf67b65b8ca
2025-03-14T05:22:35.5182698Z deleted: sha256:cea84c16acbc664f0623251d326133807364242d4aa71b4043453c1ed04e34ca
2025-03-14T05:22:35.5183081Z deleted: sha256:947031bb2881adeacf7b5f11cd5795a50586936496113c72e65c084e283c0a2e
2025-03-14T05:22:35.5183467Z deleted: sha256:22d6d8480d3a77305df9afb31c18b2dd268f4fbbf54fc12986f3845b8b1c7181
2025-03-14T05:22:35.5183854Z deleted: sha256:cd0e364d2e6c89b30a70b5d41f89e8a6c9f852c3a62a25a340d413054bfdd489
2025-03-14T05:22:35.5184302Z deleted: sha256:5bfe8c860d56369c402728193b8d376f8b0948c947641e07b0460f168551c79b
2025-03-14T05:22:35.5184688Z deleted: sha256:bc3fd16e729f1b8fba3e121a50a01a62da3545c16492a2e57b3643fe60fd3836
2025-03-14T05:22:35.5185075Z deleted: sha256:0f94ad47e1720472ec7bbb4402d3e432ae769355ffe2a2411547955af4dea876
2025-03-14T05:22:35.5185453Z deleted: sha256:70e1d9f64d002c2498c5c48a25a306eeb36b26f9ed6300be8e2ca7c79097b9b3
2025-03-14T05:22:35.5185848Z deleted: sha256:73809ebb8d79f0f0b155a8845fcea4dd4828ed8509691978b263210ee358a6a6
2025-03-14T05:22:35.5186231Z deleted: sha256:e5e7b86dfe19031f1ab2b7cb20da98206fba3b4ebdd974bfdd35d0b4b80d5377
2025-03-14T05:22:35.5186616Z deleted: sha256:c64c0c772aad31d1c419f82c9e7a5bfa822e4ca9676b669d4f1fe131ea63ec49
2025-03-14T05:22:35.5186991Z deleted: sha256:270a1170e7e398434ff1b31e17e233f7d7b71aa99a40473615860068e86720af
2025-03-14T05:22:35.5187212Z 
2025-03-14T05:22:35.5187319Z Total reclaimed space: 41.73GB
2025-03-14T05:22:35.5249962Z Post job cleanup.
2025-03-14T05:22:35.5293471Z Post job cleanup.
2025-03-14T05:22:35.6066891Z [command]/usr/bin/git version
2025-03-14T05:22:35.6103586Z git version 2.47.1
2025-03-14T05:22:35.6131120Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/d8699c9e-5d11-4a15-bf36-ffe1b50ecdb9/.gitconfig'
2025-03-14T05:22:35.6147382Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/d8699c9e-5d11-4a15-bf36-ffe1b50ecdb9' before making global git config changes
2025-03-14T05:22:35.6148122Z Adding repository directory to the temporary git global config as a safe directory
2025-03-14T05:22:35.6154363Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-14T05:22:35.6195384Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2025-03-14T05:22:35.6235659Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :"
2025-03-14T05:22:35.6541243Z Entering 'android/libs/fbjni'
2025-03-14T05:22:35.6587781Z Entering 'third_party/FP16'
2025-03-14T05:22:35.6635484Z Entering 'third_party/FXdiv'
2025-03-14T05:22:35.6686148Z Entering 'third_party/NNPACK'
2025-03-14T05:22:35.6733966Z Entering 'third_party/NVTX'
2025-03-14T05:22:35.6787326Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T05:22:35.6835212Z Entering 'third_party/XNNPACK'
2025-03-14T05:22:35.6895883Z Entering 'third_party/benchmark'
2025-03-14T05:22:35.6946098Z Entering 'third_party/composable_kernel'
2025-03-14T05:22:35.7001934Z Entering 'third_party/cpp-httplib'
2025-03-14T05:22:35.7048725Z Entering 'third_party/cpuinfo'
2025-03-14T05:22:35.7093703Z Entering 'third_party/cudnn_frontend'
2025-03-14T05:22:35.7142210Z Entering 'third_party/cutlass'
2025-03-14T05:22:35.7206525Z Entering 'third_party/eigen'
2025-03-14T05:22:35.7256604Z Entering 'third_party/fbgemm'
2025-03-14T05:22:35.7306329Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T05:22:35.7354039Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T05:22:35.7404196Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T05:22:35.7457215Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T05:22:35.7506099Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T05:22:35.7560019Z Entering 'third_party/flash-attention'
2025-03-14T05:22:35.7609838Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T05:22:35.7657294Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T05:22:35.7710911Z Entering 'third_party/flatbuffers'
2025-03-14T05:22:35.7755966Z Entering 'third_party/fmt'
2025-03-14T05:22:35.7809133Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T05:22:35.7856860Z Entering 'third_party/gloo'
2025-03-14T05:22:35.7906710Z Entering 'third_party/googletest'
2025-03-14T05:22:35.7953387Z Entering 'third_party/ideep'
2025-03-14T05:22:35.8004413Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T05:22:35.8055327Z Entering 'third_party/ittapi'
2025-03-14T05:22:35.8107944Z Entering 'third_party/kineto'
2025-03-14T05:22:35.8158641Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T05:22:35.8202538Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T05:22:35.8248423Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T05:22:35.8296359Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T05:22:35.8349729Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T05:22:35.8394406Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T05:22:35.8441665Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T05:22:35.8488587Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T05:22:35.8533911Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T05:22:35.8584142Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T05:22:35.8635642Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T05:22:35.8684382Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T05:22:35.8737799Z Entering 'third_party/kleidiai'
2025-03-14T05:22:35.8784070Z Entering 'third_party/mimalloc'
2025-03-14T05:22:35.8829391Z Entering 'third_party/nlohmann'
2025-03-14T05:22:35.8877121Z Entering 'third_party/onnx'
2025-03-14T05:22:35.8940029Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T05:22:35.8995797Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T05:22:35.9044042Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T05:22:35.9090266Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T05:22:35.9140179Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T05:22:35.9189161Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T05:22:35.9242236Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T05:22:35.9291494Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T05:22:35.9337337Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T05:22:35.9383067Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T05:22:35.9436140Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T05:22:35.9490637Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T05:22:35.9558776Z Entering 'third_party/pocketfft'
2025-03-14T05:22:35.9609905Z Entering 'third_party/protobuf'
2025-03-14T05:22:35.9658876Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T05:22:35.9705884Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T05:22:35.9756146Z Entering 'third_party/psimd'
2025-03-14T05:22:35.9808247Z Entering 'third_party/pthreadpool'
2025-03-14T05:22:35.9857654Z Entering 'third_party/pybind11'
2025-03-14T05:22:35.9909152Z Entering 'third_party/python-peachpy'
2025-03-14T05:22:35.9955294Z Entering 'third_party/sleef'
2025-03-14T05:22:36.0021219Z Entering 'third_party/tensorpipe'
2025-03-14T05:22:36.0062246Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T05:22:36.0147867Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T05:22:36.0194059Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T05:22:36.0239776Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T05:22:36.0287631Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T05:22:36.0369197Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2025-03-14T05:22:36.0390760Z http.https://github.com/.extraheader
2025-03-14T05:22:36.0399646Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader
2025-03-14T05:22:36.0427917Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :"
2025-03-14T05:22:36.0710375Z Entering 'android/libs/fbjni'
2025-03-14T05:22:36.0742095Z http.https://github.com/.extraheader
2025-03-14T05:22:36.0771774Z Entering 'third_party/FP16'
2025-03-14T05:22:36.0802593Z http.https://github.com/.extraheader
2025-03-14T05:22:36.0831993Z Entering 'third_party/FXdiv'
2025-03-14T05:22:36.0885323Z http.https://github.com/.extraheader
2025-03-14T05:22:36.0926441Z Entering 'third_party/NNPACK'
2025-03-14T05:22:36.0956623Z http.https://github.com/.extraheader
2025-03-14T05:22:36.0988954Z Entering 'third_party/NVTX'
2025-03-14T05:22:36.1021182Z http.https://github.com/.extraheader
2025-03-14T05:22:36.1052425Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T05:22:36.1084358Z http.https://github.com/.extraheader
2025-03-14T05:22:36.1114715Z Entering 'third_party/XNNPACK'
2025-03-14T05:22:36.1148338Z http.https://github.com/.extraheader
2025-03-14T05:22:36.1192855Z Entering 'third_party/benchmark'
2025-03-14T05:22:36.1222615Z http.https://github.com/.extraheader
2025-03-14T05:22:36.1253099Z Entering 'third_party/composable_kernel'
2025-03-14T05:22:36.1287708Z http.https://github.com/.extraheader
2025-03-14T05:22:36.1325002Z Entering 'third_party/cpp-httplib'
2025-03-14T05:22:36.1356161Z http.https://github.com/.extraheader
2025-03-14T05:22:36.1389217Z Entering 'third_party/cpuinfo'
2025-03-14T05:22:36.1418864Z http.https://github.com/.extraheader
2025-03-14T05:22:36.1450463Z Entering 'third_party/cudnn_frontend'
2025-03-14T05:22:36.1481958Z http.https://github.com/.extraheader
2025-03-14T05:22:36.1512344Z Entering 'third_party/cutlass'
2025-03-14T05:22:36.1544724Z http.https://github.com/.extraheader
2025-03-14T05:22:36.1583147Z Entering 'third_party/eigen'
2025-03-14T05:22:36.1613545Z http.https://github.com/.extraheader
2025-03-14T05:22:36.1644879Z Entering 'third_party/fbgemm'
2025-03-14T05:22:36.1678772Z http.https://github.com/.extraheader
2025-03-14T05:22:36.1712551Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T05:22:36.1743709Z http.https://github.com/.extraheader
2025-03-14T05:22:36.1774508Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T05:22:36.1806474Z http.https://github.com/.extraheader
2025-03-14T05:22:36.1841330Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T05:22:36.1870621Z http.https://github.com/.extraheader
2025-03-14T05:22:36.1911077Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T05:22:36.1940341Z http.https://github.com/.extraheader
2025-03-14T05:22:36.1975383Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T05:22:36.2005468Z http.https://github.com/.extraheader
2025-03-14T05:22:36.2042736Z Entering 'third_party/flash-attention'
2025-03-14T05:22:36.2072169Z http.https://github.com/.extraheader
2025-03-14T05:22:36.2105851Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T05:22:36.2134208Z http.https://github.com/.extraheader
2025-03-14T05:22:36.2178799Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T05:22:36.2208499Z http.https://github.com/.extraheader
2025-03-14T05:22:36.2252539Z Entering 'third_party/flatbuffers'
2025-03-14T05:22:36.2284018Z http.https://github.com/.extraheader
2025-03-14T05:22:36.2321047Z Entering 'third_party/fmt'
2025-03-14T05:22:36.2350893Z http.https://github.com/.extraheader
2025-03-14T05:22:36.2388596Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T05:22:36.2414759Z http.https://github.com/.extraheader
2025-03-14T05:22:36.2446453Z Entering 'third_party/gloo'
2025-03-14T05:22:36.2479313Z http.https://github.com/.extraheader
2025-03-14T05:22:36.2513664Z Entering 'third_party/googletest'
2025-03-14T05:22:36.2542985Z http.https://github.com/.extraheader
2025-03-14T05:22:36.2573595Z Entering 'third_party/ideep'
2025-03-14T05:22:36.2606429Z http.https://github.com/.extraheader
2025-03-14T05:22:36.2635526Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T05:22:36.2668395Z http.https://github.com/.extraheader
2025-03-14T05:22:36.2709646Z Entering 'third_party/ittapi'
2025-03-14T05:22:36.2744102Z http.https://github.com/.extraheader
2025-03-14T05:22:36.2775658Z Entering 'third_party/kineto'
2025-03-14T05:22:36.2806980Z http.https://github.com/.extraheader
2025-03-14T05:22:36.2841189Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T05:22:36.2872580Z http.https://github.com/.extraheader
2025-03-14T05:22:36.2904511Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T05:22:36.2935359Z http.https://github.com/.extraheader
2025-03-14T05:22:36.2972146Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T05:22:36.3003652Z http.https://github.com/.extraheader
2025-03-14T05:22:36.3040453Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T05:22:36.3069061Z http.https://github.com/.extraheader
2025-03-14T05:22:36.3105768Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T05:22:36.3133567Z http.https://github.com/.extraheader
2025-03-14T05:22:36.3171250Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T05:22:36.3202952Z http.https://github.com/.extraheader
2025-03-14T05:22:36.3243010Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T05:22:36.3272347Z http.https://github.com/.extraheader
2025-03-14T05:22:36.3308393Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T05:22:36.3346733Z http.https://github.com/.extraheader
2025-03-14T05:22:36.3376110Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T05:22:36.3404415Z http.https://github.com/.extraheader
2025-03-14T05:22:36.3440604Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T05:22:36.3469016Z http.https://github.com/.extraheader
2025-03-14T05:22:36.3506375Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T05:22:36.3544174Z http.https://github.com/.extraheader
2025-03-14T05:22:36.3573039Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T05:22:36.3603331Z http.https://github.com/.extraheader
2025-03-14T05:22:36.3638742Z Entering 'third_party/kleidiai'
2025-03-14T05:22:36.3671532Z http.https://github.com/.extraheader
2025-03-14T05:22:36.3706640Z Entering 'third_party/mimalloc'
2025-03-14T05:22:36.3738700Z http.https://github.com/.extraheader
2025-03-14T05:22:36.3770876Z Entering 'third_party/nlohmann'
2025-03-14T05:22:36.3799312Z http.https://github.com/.extraheader
2025-03-14T05:22:36.3832914Z Entering 'third_party/onnx'
2025-03-14T05:22:36.3865464Z http.https://github.com/.extraheader
2025-03-14T05:22:36.3909672Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T05:22:36.3943250Z http.https://github.com/.extraheader
2025-03-14T05:22:36.3979766Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T05:22:36.4007688Z http.https://github.com/.extraheader
2025-03-14T05:22:36.4046327Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T05:22:36.4073476Z http.https://github.com/.extraheader
2025-03-14T05:22:36.4110592Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T05:22:36.4137206Z http.https://github.com/.extraheader
2025-03-14T05:22:36.4174880Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T05:22:36.4203524Z http.https://github.com/.extraheader
2025-03-14T05:22:36.4238154Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T05:22:36.4269864Z http.https://github.com/.extraheader
2025-03-14T05:22:36.4306172Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T05:22:36.4332990Z http.https://github.com/.extraheader
2025-03-14T05:22:36.4370180Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T05:22:36.4401869Z http.https://github.com/.extraheader
2025-03-14T05:22:36.4437312Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T05:22:36.4469627Z http.https://github.com/.extraheader
2025-03-14T05:22:36.4503260Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T05:22:36.4533321Z http.https://github.com/.extraheader
2025-03-14T05:22:36.4571143Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T05:22:36.4599479Z http.https://github.com/.extraheader
2025-03-14T05:22:36.4640088Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T05:22:36.4671587Z http.https://github.com/.extraheader
2025-03-14T05:22:36.4725359Z Entering 'third_party/pocketfft'
2025-03-14T05:22:36.4754740Z http.https://github.com/.extraheader
2025-03-14T05:22:36.4791583Z Entering 'third_party/protobuf'
2025-03-14T05:22:36.4820783Z http.https://github.com/.extraheader
2025-03-14T05:22:36.4854504Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T05:22:36.4887388Z http.https://github.com/.extraheader
2025-03-14T05:22:36.4924992Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T05:22:36.4959132Z http.https://github.com/.extraheader
2025-03-14T05:22:36.4998751Z Entering 'third_party/psimd'
2025-03-14T05:22:36.5029600Z http.https://github.com/.extraheader
2025-03-14T05:22:36.5067590Z Entering 'third_party/pthreadpool'
2025-03-14T05:22:36.5097004Z http.https://github.com/.extraheader
2025-03-14T05:22:36.5134552Z Entering 'third_party/pybind11'
2025-03-14T05:22:36.5165839Z http.https://github.com/.extraheader
2025-03-14T05:22:36.5198224Z Entering 'third_party/python-peachpy'
2025-03-14T05:22:36.5229899Z http.https://github.com/.extraheader
2025-03-14T05:22:36.5265938Z Entering 'third_party/sleef'
2025-03-14T05:22:36.5293351Z http.https://github.com/.extraheader
2025-03-14T05:22:36.5331178Z Entering 'third_party/tensorpipe'
2025-03-14T05:22:36.5366135Z http.https://github.com/.extraheader
2025-03-14T05:22:36.5396005Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T05:22:36.5424993Z http.https://github.com/.extraheader
2025-03-14T05:22:36.5458492Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T05:22:36.5490407Z http.https://github.com/.extraheader
2025-03-14T05:22:36.5524054Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T05:22:36.5556591Z http.https://github.com/.extraheader
2025-03-14T05:22:36.5589015Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T05:22:36.5621864Z http.https://github.com/.extraheader
2025-03-14T05:22:36.5654007Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T05:22:36.5687686Z http.https://github.com/.extraheader
2025-03-14T05:22:36.5838026Z A job completed hook has been configured by the self-hosted runner administrator
2025-03-14T05:22:36.5862947Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh'
2025-03-14T05:22:36.5866207Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:22:36.5866487Z ##[endgroup]
2025-03-14T05:22:36.5938065Z [!ALERT!] Swap in detected! [!ALERT!]
2025-03-14T05:22:45.0274588Z [!ALERT!] Swap out detected [!ALERT!]
2025-03-14T05:22:58.4905839Z Cleaning up orphan processes